Modeling

and

Predicting

Personal

Information

Dissemination

Behavior

In

this

paper

we

propose

a

new

way

to

automatically

model

and

predict

human

behavior

of

receiving

and

disseminating

information

by

analyzing

the

contact

and

content

of

personal

communications

A

personal

profile

called

CommunityNet

is

established

for

each

individual

based

on

a

novel

algorithm

incorporating

contact

content

and

time

information

simultaneously

It

can

be

used

for

personal

social

capital

management

Clusters

of

CommunityNets

provide

a

view

of

informal

networks

for

organization

management

Our

new

algorithm

is

developed

based

on

the

combination

of

dynamic

algorithms

in

the

social

network

field

and

the

semantic

content

classification

methods

in

the

natural

language

processing

and

machine

learning

literatures

We

tested

CommunityNets

on

the

Enron

Email

corpus

and

report

experimental

results

including

filtering

prediction

and

recommendation

capabilities

We

show

that

the

personal

behavior

and

intention

are

somewhat

predictable

based

on

these

models

For

instance

to

whom

a

person

is

going

to

send

a

specific

email

can

be

predicted

by

one

's

personal

social

network

and

content

analysis

Experimental

results

show

the

prediction

accuracy

of

the

proposed

adaptive

algorithm

is

better

than

the

social

network-based

predictions

and

is

better

than

an

aggregated

model

based

on

Latent

Dirichlet

Allocation

with

social

network

enhancement

Two

online

demo

systems

we

developed

that

allow

interactive

exploration

of

CommunityNet

are

also

discussed

I.

-LSB-

Artificial

Intelligence

-RSB-

Learning

General

Terms

algorithms

experimentation

Working

in

the

information

age

the

most

important

is

not

what

you

know

but

who

you

know

-LSB-

-RSB-

A

social

network

the

graph

of

relationships

and

interactions

within

a

group

of

individuals

plays

a

fundamental

role

as

a

medium

for

the

spread

of

information

ideas

and

influence

At

the

organizational

level

personal

social

networks

are

activated

for

recruitment

partnering

and

information

access

At

the

individual

level

people

exploit

their

networks

to

advance

careers

and

gather

information

Informal

network

within

formal

organizations

is

a

major

but

hard

to

acquire

factor

affecting

companies

performance

Krackhardt

-LSB-

-RSB-

showed

that

companies

with

strong

informal

networks

perform

five

or

six

times

better

than

those

with

weak

networks

especially

on

the

long-term

performance

Friend

and

advice

networks

drive

enterprise

operations

in

a

way

that

if

the

real

organization

structure

does

not

match

the

informal

networks

then

a

company

tends

to

fail

-LSB-

-RSB-

Since

Max

Weber

first

studied

modern

bureaucracy

structures

in

the

decades

of

related

social

scientific

researches

have

been

mainly

relying

on

questionnaires

and

interviews

to

understand

individuals

thoughts

and

behaviors

for

sensing

informal

networks

However

data

collection

is

time

consuming

and

seldom

provides

timely

continuous

and

dynamic

information

This

is

usually

the

biggest

hurdle

in

social

studies

Personal

Social

Network

-LRB-

PSN

-RRB-

could

provide

an

organizing

principle

for

advanced

user

interfaces

that

offer

information

management

and

communication

services

in

a

single

integrated

system

One

of

the

most

pronounced

examples

is

the

networking

study

by

Nardi

et

al.

-LSB-

-RSB-

who

coined

the

term

intensional

networks

to

describe

personal

social

networks

They

presented

a

visual

model

of

user

's

PSN

to

organize

personal

communications

in

terms

of

a

social

network

of

contacts

From

this

perspective

many

tools

were

built

such

as

LinkedIn

-LSB-

-RSB-

Orkut

-LSB-

-RSB-

and

Friendster

-LSB-

-RSB-

However

all

of

them

only

provide

tools

for

visually

managing

personal

social

networks

Users

need

to

manually

input

update

and

manage

these

networks

This

results

in

serious

drawbacks

For

instance

people

may

not

be

able

to

invest

necessary

efforts

in

creating

rich

information

or

they

may

not

keep

the

information

up-to-date

as

their

interests

responsibilities

and

network

change

They

need

a

way

to

organize

the

relationship

and

remember

who

have

the

resources

to

help

them

We

coin

the

terminology

of

managing

these

goals

as

personal

social

capital

management

In

this

paper

we

develop

a

user-centric

modeling

technology

which

can

dynamically

describe

and

update

a

Social

capital

infers

to

the

accumulated

contacts

human

capital

-LRB-

asset

power

resources

-RRB-

that

a

person

can

explore

through

social

network

-LSB-

-RSB-

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

or

republish

to

post

on

servers

or

to

redistribute

to

lists

requires

prior

specific

permission

and/or

a

fee

KDD

August

Chicago

Illinois

USA

Copyright

ACM

Industry/Government

Track

Paper

person

's

personal

social

network

with

context-dependent

and

temporal

evolution

information

from

personal

communications

We

refer

to

the

model

as

a

CommunityNet

Senders

and

receivers

time

stamps

subject

and

content

of

emails

contribute

three

key

components

content

semantics

temporal

information

and

social

relationship

We

propose

a

novel

Content-Time-Relation

-LRB-

CTR

-RRB-

algorithm

to

capture

dynamic

and

context-dependent

information

in

an

unsupervised

way

Based

on

the

CommunityNet

models

many

questions

can

be

addressed

by

inference

prediction

and

filtering

For

instance

-RRB-

Who

are

semantically

related

to

each

other

-RRB-

Who

will

be

involved

in

a

special

topic

Who

are

the

important

-LRB-

central

-RRB-

people

in

this

topic

-RRB-

How

does

the

information

flow

and

-RRB-

If

we

want

to

publicize

a

message

whom

should

we

inform

Figure

shows

the

procedure

of

our

proposed

scheme

First

topic

detection

and

clustering

is

conducted

on

training

emails

in

order

to

define

topic-communities

Then

for

each

individual

CommunityNet

is

built

based

on

the

detected

topics

the

sender

and

receiver

information

and

the

time

stamps

Afterwards

these

personal

CommunityNets

can

be

applied

for

inferring

organizational

informal

networks

and

predicting

personal

behaviors

to

help

users

manage

their

social

capitals

We

incorporate

the

following

innovative

steps

-RRB-

Incorporate

content

analysis

into

social

network

in

an

unsupervised

way

-RRB-

Build

a

CommunityNet

for

each

user

to

capture

the

context-dependent

temporal

evolutionary

personal

social

network

based

on

email

communication

records

-RRB-

Analyze

people

's

behaviors

based

on

CommunityNet

including

predicting

people

's

information

sending

and

receiving

behaviors

-RRB-

Show

the

potential

of

using

automatically

acquired

personal

social

network

for

organization

and

personal

social

capital

management

Input

Emails

From

sally.beck@enron.com

To

shona.wilson@enron.com

Subject

Re

timing

of

submitting

information

to

Risk

Controls

Good

memo

let

me

know

if

you

see

results

Topic

Detection

Content

Analysis

Topics

Meeting

schedule

Agreement

California

Energy

Game

Holiday

celebration

CommunityNet

CommunityNet

Modeling

Applications

Recommendation

system

Prediction

Filtering

Input

Emails

From

sally.beck@enron.com

To

shona.wilson@enron.com

Subject

Re

timing

of

submitting

information

to

Risk

Controls

Good

memo

let

me

know

if

you

see

results

Topic

Detection

Content

Analysis

Topics

Meeting

schedule

Agreement

California

Energy

Game

Holiday

celebration

CommunityNet

CommunityNet

Modeling

Applications

Recommendation

system

Prediction

Filtering

Figure

An

Overview

of

CommunityNet

We

tested

the

CommunityNet

model

on

the

Enron

email

corpus

comprising

the

communication

records

of

Enron

employees

dating

from

Jan.

to

Aug.

The

Enron

email

dataset

was

originally

made

available

to

public

by

the

Federal

Energy

Regulatory

Commission

during

the

investigation

-LSB-

-RSB-

It

was

later

collected

and

prepared

by

Melinda

Gervasio

at

SRI

for

the

CALO

-LRB-

A

Cognitive

Assistant

that

Learns

and

Organizes

-RRB-

project

William

Cohen

from

CMU

has

put

up

the

dataset

on

the

web

for

research

purpose

-LSB-

-RSB-

This

version

of

the

dataset

contains

around

emails

within

folders

We

clean

the

data

and

extract

users

from

those

folders

with

unique

messages

from

to

In

the

experiments

we

use

intra-organizational

emails

which

connect

these

people

The

primary

contributions

of

this

paper

are

three-fold

First

we

develop

an

algorithm

incorporating

content-time-relation

detection

Second

we

generate

an

application

model

which

describes

personal

dynamic

community

network

Third

we

show

how

this

model

can

be

applied

to

organization

and

social

capital

management

To

the

best

of

our

knowledge

this

is

among

the

first

reported

technologies

on

fusing

research

in

the

social

network

analysis

field

and

the

content

analysis

field

for

information

management

We

propose

the

CTR

algorithm

and

the

CommunityNet

based

on

the

Latent

Dirichlet

Allocation

algorithm

In

our

experiments

we

observed

clear

benefit

of

discovering

knowledge

based

on

multi-modality

information

rather

than

using

only

single

type

of

data

The

rest

of

the

paper

is

organized

as

follows

In

Section

we

present

an

overview

of

related

work

In

Section

we

present

our

model

We

discuss

how

to

use

CommunityNet

to

analyze

communities

and

individuals

in

section

and

respectively

In

Section

we

show

two

demo

systems

for

query

visualization

and

contact

recommendation

Finally

conclusions

and

future

work

are

addressed

in

Section

To

capture

relationships

between

entities

social

network

has

been

a

subject

of

study

for

more

than

years

An

early

sign

of

the

potential

of

social

network

was

perhaps

the

classic

paper

by

Milgram

-LSB-

-RSB-

estimating

that

on

average

every

person

in

the

world

is

only

six

edges

away

from

each

other

if

an

edge

between

i

and

j

means

i

knows

j

Lately

introducing

social

network

analysis

into

information

mining

is

becoming

an

important

research

area

Schwartz

and

Wood

-LSB-

-RSB-

mined

social

relationships

from

email

logs

by

using

a

set

of

heuristic

graph

algorithms

The

Referral

Web

project

-LSB-

-RSB-

mined

a

social

network

from

a

wide

variety

of

publicly-available

online

information

and

used

it

to

help

individuals

find

experts

who

could

answer

their

questions

based

on

geographical

proximity

Flake

et

al.

-LSB-

-RSB-

used

graph

algorithms

to

mine

communities

from

the

Web

-LRB-

defined

as

sets

of

sites

that

have

more

links

to

each

other

than

to

non-members

-RRB-

Tyler

et

al.

-LSB-

-RSB-

use

a

betweenness

centrality

algorithm

for

the

automatic

identification

of

communities

of

practice

from

email

logs

within

an

organization

The

Google

search

engine

-LSB-

-RSB-

and

Kleinberg

's

HITS

algorithm

of

finding

hubs

and

authorities

on

the

Web

-LSB-

-RSB-

are

also

based

on

social

network

concepts

The

success

of

these

approaches

and

the

discovery

of

widespread

network

topologies

with

nontrivial

properties

have

led

to

a

recent

flurry

of

research

on

applying

link

analysis

for

information

mining

A

promising

class

of

statistical

models

for

expressing

structural

properties

of

social

networks

is

the

class

of

Exponential

Random

Graph

Models

-LRB-

ERGMs

-RRB-

-LRB-

or

p

model

-RRB-

-LSB-

-RSB-

This

statistical

model

can

represent

structural

properties

that

define

complicated

dependence

patterns

that

can

not

be

easily

modeled

by

deterministic

models

Let

Y

denote

a

random

graph

on

a

set

of

n

nodes

and

let

y

denote

a

particular

graph

on

those

nodes

Then

the

probability

of

Y

equals

to

y

is

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

exp

T

s

y

P

Y

y

c

-LRB-

-RRB-

Industry/Government

Track

Paper

where

-LRB-

-RRB-

s

y

is

a

known

vector

of

graph

statistics

-LRB-

Density

Reciprocity

Transitivity

etc

-RRB-

on

y

is

a

vector

of

coefficients

to

model

the

influence

of

each

statistics

for

the

whole

graph

T

means

transpose

-LRB-

-RRB-

c

is

a

normalization

term

to

satisfy

-LRB-

-RRB-

y

P

Y

y

The

parameters

are

estimated

based

on

the

observed

graph

obs

y

by

maximum

likelihood

estimation

All

the

research

discussed

above

has

focused

on

using

static

properties

in

a

network

to

represent

the

complex

structure

However

social

networks

evolve

over

time

Evolution

property

has

a

great

deal

of

influence

e.g.

it

affects

the

rate

of

information

diffusion

the

ability

to

acquire

and

use

information

and

the

quality

and

accuracy

of

organizational

decisions

Dynamics

of

social

networks

have

attracted

many

researchers

attentions

recently

Given

a

snapshot

of

a

social

network

-LSB-

-RSB-

tries

to

infer

which

new

interactions

among

its

members

are

likely

to

occur

in

the

near

future

In

-LSB-

-RSB-

Kubica

et

al.

are

interested

in

tracking

changes

in

large-scale

data

by

periodically

creating

an

agglomerative

clustering

and

examining

the

evolution

of

clusters

over

time

Among

the

known

dynamical

social

networks

in

literature

Snijder

's

dynamic

actor-oriented

social

network

-LSB-

-RSB-

is

one

of

the

most

successful

algorithms

Changes

in

the

network

are

modeled

as

the

stochastic

result

of

network

effects

-LRB-

density

reciprocity

etc.

-RRB-

Evolution

is

modeled

by

continuous-time

Markov

chains

whose

parameters

are

estimated

by

the

Markov

chain

Monte

Carlo

procedures

In

-LSB-

-RSB-

Handcock

et

al.

proposed

a

curved

ERGM

model

and

applied

it

to

the

new

specifications

of

ERGMs

This

latest

model

uses

nonlinear

parameters

to

represent

structural

properties

of

networks

The

above

mentioned

dynamic

analyses

show

some

success

in

analyzing

longitudinal

stream

data

However

most

of

them

are

only

based

on

pure

network

properties

without

knowing

what

people

are

talking

about

and

why

they

have

close

relationships

Content

Analysis

In

statistical

Natural

Language

processing

one

common

way

of

modeling

the

contributions

of

different

topics

to

a

document

is

to

treat

each

topic

as

a

probability

distribution

over

words

viewing

a

document

as

a

probability

distribution

over

words

and

thus

viewing

a

document

as

a

probabilistic

mixture

over

these

topics

Given

T

topics

the

probability

of

the

ith

word

in

a

given

document

is

formalized

as

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

T

i

i

i

i

j

P

w

P

w

z

j

P

z

j

-LRB-

-RRB-

where

i

z

is

a

latent

variable

indicating

the

topic

from

which

the

i

th

word

was

drawn

and

-LRB-

-RRB-

i

i

P

w

z

j

is

the

probability

of

the

word

i

w

under

the

jth

topic

-LRB-

-RRB-

i

P

z

j

gives

the

probability

of

choosing

a

word

from

topics

j

in

the

current

document

which

varies

across

different

documents

Hofmann

-LSB-

-RSB-

introduced

the

aspect

model

Probabilistic

Latent

Semantic

Analysis

-LRB-

PLSA

-RRB-

in

which

topics

are

modeled

as

multinomial

distributions

over

words

and

documents

are

assumed

to

be

generated

by

the

activation

of

multiple

topics

Blei

et

al.

-LSB-

-RSB-

proposed

Latent

Dirichlet

Allocation

-LRB-

LDA

-RRB-

to

address

the

problems

of

PLSA

that

parameterization

was

susceptible

to

overfitting

and

did

not

provide

a

straightforward

way

to

infer

testing

documents

A

distribution

over

topics

is

sampled

from

a

Dirichlet

distribution

for

each

document

Each

word

is

sampled

from

a

multinomial

distribution

over

words

specific

to

the

sampled

topic

Following

the

notations

in

-LSB-

-RSB-

in

LDA

D

documents

containing

T

topics

expressed

over

W

unique

words

we

can

represent

-LRB-

-RRB-

P

w

z

with

a

set

of

T

multinomial

distributions

over

the

W

words

such

that

-LRB-

-RRB-

-LRB-

-RRB-

w

j

P

w

z

j

and

P

-LRB-

z

-RRB-

with

a

set

of

D

multinomial

distribution

over

the

T

topics

such

that

for

a

word

in

document

d

-LRB-

-RRB-

-LRB-

-RRB-

d

j

P

z

j

Recently

the

Author-Topic

-LRB-

AT

-RRB-

model

-LSB-

-RSB-

extends

LDA

to

include

authorship

information

trying

to

recognize

which

part

of

the

document

is

contributed

by

which

co-author

In

a

recent

unpublished

work

McCallum

et

al.

-LSB-

-RSB-

further

extend

the

AT

model

to

the

Author-Recipient-Topic

model

by

regarding

the

sender-receiver

pair

as

an

additional

author

variable

for

topic

classification

Their

goal

is

role

discovery

which

is

similar

to

one

of

our

goals

as

discussed

in

Sec.

without

taking

the

temporal

nature

of

emails

into

consideration

Using

LDA

and

are

parameters

that

need

to

be

estimated

by

using

sophisticated

approximation

either

with

variational

Bayes

or

expectation

propagation

To

solve

this

problem

Griffiths

and

Steyvers

-LSB-

-RSB-

extended

LDA

by

considering

the

posterior

distribution

over

the

assignments

of

words

to

topics

and

showed

how

Gibbs

sampling

could

be

applied

to

build

models

Specifically

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

i

i

i

w

d

i

j

i

j

i

i

d

i

j

i

n

n

P

z

j

z

w

n

W

n

T

-LRB-

-RRB-

where

-LRB-

-RRB-

i

n

is

a

count

that

does

not

include

the

current

assignment

-LRB-

-RRB-

w

j

n

is

the

number

of

times

word

w

has

been

assigned

to

topic

j

in

the

vector

of

assignments

z

-LRB-

-RRB-

d

j

n

is

the

number

of

times

a

word

from

document

d

has

been

assigned

to

topic

j

-LRB-

-RRB-

j

n

is

a

sum

of

-LRB-

-RRB-

w

j

n

-LRB-

-RRB-

d

n

is

a

sum

of

-LRB-

-RRB-

d

j

n

Further

one

can

estimate

-LRB-

-RRB-

w

j

the

probability

of

using

word

w

in

topic

j

and

-LRB-

-RRB-

d

j

the

probability

of

topic

j

in

document

d

as

follows

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

w

w

j

j

j

n

n

W

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

d

d

j

j

d

n

n

T

-LRB-

-RRB-

In

-LSB-

-RSB-

experiments

show

that

topics

can

be

recovered

by

their

algorithm

and

show

meaningful

aspects

of

the

structure

and

relationships

between

scientific

papers

Contextual

relational

and

temporal

information

are

three

key

factors

for

current

data

mining

and

knowledge

management

models

However

there

are

few

papers

addressing

these

three

components

simultaneously

In

our

recent

paper

we

built

user

models

to

explicitly

describe

a

person

's

expertise

by

a

relational

and

evolutionary

graph

representation

called

ExpertisetNet

-LSB-

-RSB-

In

this

paper

we

continue

exploring

this

thread

and

build

a

CommunityNet

model

which

incorporates

these

three

components

together

for

data

mining

and

knowledge

management

In

this

section

we

first

define

terminologies

Then

we

propose

a

Content-Time-Relation

-LRB-

CTR

-RRB-

algorithm

to

build

the

Industry/Government

Track

Paper

personal

CommunityNet

We

also

specifically

address

the

prediction

of

the

user

's

behaviors

as

a

classification

problem

and

solve

it

based

on

the

CommunityNet

models

Terminology

Definition

Topic-Community

A

topic

community

is

a

group

of

people

who

participate

in

one

specific

topic

Definition

Personal

Topic-Community

Network

-LRB-

PTCN

-RRB-

A

personal

topic-community

network

is

a

group

of

people

directly

connected

to

one

person

about

a

specific

topic

Definition

Evolutionary

Personal

Social

Network

An

evolutionary

personal

social

network

illustrates

how

a

personal

social

network

changes

over

time

Definition

Evolutionary

Personal

Topic-Community

Network

An

evolutionary

network

illustrates

how

a

person

's

personal

topic-community

network

changes

over

time

Definition

Personal

Social

Network

Information

Flow

A

personal

social

network

information

flow

illustrates

how

the

information

flows

over

a

person

's

personal

social

network

to

other

people

's

personal

social

networks

Definition

Personal

Topic-Community

Information

Flow

A

personal

Topic-CommunityNet

information

flow

illustrates

how

the

information

about

one

topic

flows

over

a

person

's

personal

social

network

to

other

people

's

personal

social

networks

Personal

Social

Network

We

build

people

's

personal

social

networks

by

collecting

their

communication

records

The

nodes

of

a

network

represent

whom

this

person

contacts

with

The

weights

of

the

links

measure

the

probabilities

of

the

emails

he

sends

to

the

other

people

A

basic

form

of

the

probability

that

an

user

u

sending

email

to

a

recipient

r

is

-LRB-

-RRB-

number

of

times

sends

emails

to

total

number

of

emails

sent

out

by

u

r

P

r

u

u

-LRB-

-RRB-

We

build

evolutionary

personal

social

networks

to

explore

the

dynamics

and

the

evolution

The

ERGM

in

Eq

-LRB-

-RRB-

can

be

used

to

replace

Eq

-LRB-

-RRB-

for

probabilistic

graph

modeling

A

big

challenge

of

automatically

building

evolutionary

personal

social

network

is

the

evolutionary

segmentation

which

is

to

detect

changes

between

personal

social

network

cohesive

sections

Here

we

apply

the

same

algorithm

as

we

proposed

in

-LSB-

-RSB-

For

each

personal

social

network

in

one

time

period

t

we

use

the

exponential

random

graph

model

-LSB-

-RSB-

to

estimate

an

underlying

distribution

to

describe

the

social

network

An

ERGM

is

estimated

from

the

data

in

each

temporal

sliding

window

With

these

operations

we

obtain

a

series

of

parameters

which

indicates

the

graph

configurations

Content-Time-Relation

Algorithm

We

begin

with

email

content

sender

and

receiver

information

and

time

stamps

and

use

these

sources

of

knowledge

to

create

a

joint

probabilistic

model

An

observation

is

-LRB-

u

r

d

w

t

-RRB-

corresponds

to

an

event

of

a

user

u

sending

to

receivers

r

an

email

d

containing

words

w

during

a

particular

time

period

t.

Conceptually

users

choose

latent

topics

z

which

in

turn

generate

receivers

r

documents

d

and

their

content

words

w

during

time

period

t.

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

z

P

u

r

d

t

P

u

r

z

t

P

z

d

t

-LRB-

-RRB-

where

u

r

is

a

sender-receiver

pair

during

time

period

t

u

r

can

be

replaced

by

any

variable

to

indicate

the

user

's

behavior

as

long

as

it

is

also

assumed

to

be

dependent

on

latent

topics

of

emails

In

order

to

model

the

PTCN

one

challenge

is

how

to

detect

latent

topics

dynamically

and

at

the

same

time

track

the

emails

related

to

the

old

topics

This

is

a

problem

similar

to

topic

detection

tracking

-LSB-

-RSB-

We

propose

an

incremental

LDA

-LRB-

ILDA

-RRB-

algorithm

to

solve

it

in

which

the

number

of

topics

is

dynamically

updated

based

on

the

Bayesian

model

selection

principle

-LSB-

-RSB-

The

procedures

of

the

algorithm

are

illustrated

as

follows

Incremental

Latent

Dirichlet

Allocation

-LRB-

ILDA

-RRB-

algorithm

Input

Email

streams

with

timestamp

t

Output

-LRB-

-RRB-

w

j

t

-LRB-

-RRB-

d

j

t

for

different

time

period

t

Steps

-RRB-

Apply

LDA

on

a

data

set

with

currently

observed

emails

in

a

time

period

t

to

generate

latent

topics

j

z

and

estimate

-LRB-

-RRB-

-LRB-

-RRB-

w

j

j

t

P

w

z

t

and

-LRB-

-RRB-

-LRB-

-RRB-

d

j

j

t

P

z

d

t

by

equation

-LRB-

-RRB-

and

-LRB-

-RRB-

The

number

of

topics

is

determined

by

the

Bayesian

model

selection

principle

-RRB-

When

new

emails

arrive

during

time

period

k

use

Bayesian

model

selection

principle

to

determine

the

number

of

topics

and

apply

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

i

i

d

i

j

i

i

k

i

k

d

i

n

P

z

j

z

w

t

P

w

z

j

t

n

T

to

estimate

-LRB-

-RRB-

k

P

z

d

t

-LRB-

-RRB-

k

P

w

z

t

and

-LRB-

-RRB-

k

P

z

w

t

-RRB-

Repeat

step

-RRB-

until

no

data

arrive

Based

on

this

ILDA

algorithm

we

propose

a

Content-Time-Relation

-LRB-

CTR

-RRB-

algorithm

It

consists

of

two

phases

the

training

phase

and

the

testing

phase

In

the

training

phase

emails

as

well

as

the

senders

receivers

and

time

stamps

are

available

-LRB-

-RRB-

old

P

w

z

t

and

-LRB-

-RRB-

old

P

u

r

z

t

are

learnt

from

the

observed

data

In

the

testing

phase

we

apply

ILDA

to

learn

-LRB-

-RRB-

new

P

z

d

t

Based

on

-LRB-

-RRB-

old

P

u

r

z

t

which

is

learnt

from

the

training

phase

u

r

can

be

inferred

Again

u

r

represents

a

sender-receiver

pair

or

any

variable

to

indicate

the

user

's

behavior

as

long

as

it

is

dependent

on

the

latent

topics

of

emails

Content-Time-Relation

-LRB-

CTR

-RRB-

algorithm

-RRB-

Training

phase

Input

Old

emails

with

content

sender

and

receiver

information

and

time

stamps

old

t

Output

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

and

old

old

old

P

w

z

t

P

z

d

t

P

u

r

z

t

Steps

a

-RRB-

Apply

Gibbs

Sampling

on

the

data

according

to

equation

-LRB-

-RRB-

b

-RRB-

Estimate

-LRB-

-RRB-

-LRB-

-RRB-

old

w

j

old

j

t

P

w

z

t

and

-LRB-

-RRB-

-LRB-

-RRB-

old

d

j

old

j

t

P

z

d

t

by

equation

-LRB-

-RRB-

and

-LRB-

-RRB-

c

-RRB-

Estimate

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

old

old

old

d

old

old

d

P

u

r

z

t

P

u

r

d

t

P

d

z

t

P

u

r

d

t

P

z

d

t

-LRB-

-RRB-

-RRB-

Testing

phase

Input

New

emails

with

content

and

time

stamps

new

t

Industry/Government

Track

Paper

Output

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

and

new

new

new

P

u

r

d

t

P

w

z

t

P

z

d

t

Steps

a

-RRB-

Apply

incremental

LDA

by

Gibbs

Sampling

based

on

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

i

i

d

i

j

i

i

new

i

old

d

i

n

P

z

j

z

w

t

P

w

z

j

t

n

T

to

estimate

-LRB-

-RRB-

j

new

P

w

z

t

and

-LRB-

-RRB-

new

P

z

d

t

by

equation

-LRB-

-RRB-

and

-LRB-

-RRB-

b

-RRB-

If

the

topics

are

within

the

training

set

estimate

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

new

old

new

z

P

u

r

d

t

P

u

r

z

t

P

z

d

t

else

if

the

sender

and

receivers

are

within

the

training

set

estimate

-LRB-

-RRB-

new

P

u

r

d

t

by

topic-independent

social

network

-LRB-

-RRB-

old

P

u

r

t

c

-RRB-

If

there

are

new

topics

detected

update

the

model

by

incorporating

the

new

topics

Inference

filtering

and

prediction

can

be

conducted

based

on

this

model

For

the

CTR

algorithm

sender

variable

u

or

receiver

variable

r

is

fixed

For

instance

if

we

are

interested

in

-LRB-

-RRB-

P

r

u

d

t

which

is

to

answer

a

question

of

whom

we

should

send

the

message

d

to

during

the

time

period

t

The

answer

will

be

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

argmax

argmax

old

old

new

t

t

t

old

new

old

new

r

t

old

t

new

old

t

new

r

z

z

z

P

r

u

d

t

P

r

u

z

t

P

z

u

d

t

P

r

u

t

P

z

u

d

t

-LRB-

-RRB-

where

z

new

old

t

t

z

represents

the

new

topics

emerging

during

the

time

period

t.

Another

question

is

if

we

receive

an

email

who

will

be

possibly

the

sender

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

argmax

argmax

old

old

old

t

t

t

old

new

old

new

u

t

old

t

new

old

t

new

u

z

z

z

P

u

r

d

t

P

u

r

z

t

P

z

r

d

t

P

u

r

t

P

z

r

d

t

-LRB-

-RRB-

Eq

-LRB-

-RRB-

and

Eq

-LRB-

-RRB-

integrate

the

PSN

content

and

temporal

analysis

Social

network

models

such

as

ERGM

in

Eq

-LRB-

-RRB-

or

the

model

in

Sec.

can

be

applied

to

the

-LRB-

-RRB-

P

u

r

d

t

terms

Figure

illustrates

the

CTR

model

and

compares

to

the

LDA

AT

and

ART

models

In

CTR

the

observed

variables

not

only

include

the

words

w

in

an

email

but

also

the

sender

u

and

the

timestamp

on

each

email

d.

Predictive

Algorithms

For

the

sake

of

easier

evaluation

we

focus

on

prediction

schemes

in

details

Specifically

we

address

the

problem

of

predicting

receivers

and

senders

of

emails

as

a

classification

problem

in

which

we

train

classifiers

to

predict

the

senders

or

receivers

and

other

behavior

patterns

given

the

observed

people

's

communication

records

The

trained

classifier

represents

a

function

in

the

form

of

-LRB-

-RRB-

f

Comm

t

i

t

Y

-LRB-

-RRB-

where

-LRB-

-RRB-

Comm

t

i

t

is

the

observed

communication

record

during

the

interval

from

time

t-i

to

t

Y

is

a

set

of

receivers

or

senders

or

other

user

behavior

patterns

to

be

discriminated

and

the

value

of

-LRB-

-RRB-

-LRB-

-RRB-

f

Comm

t

i

t

is

the

classifier

prediction

regarding

which

user

behavior

patterns

gave

rise

to

the

observed

communication

records

The

classifier

is

trained

by

providing

the

history

of

the

communication

records

with

known

user

behaviors

Using

Personal

Social

Network

Model

We

aggregate

all

the

communication

records

in

the

history

of

a

given

user

and

build

his/her

personal

social

network

We

choose

those

people

with

the

highest

communication

frequency

with

this

person

as

the

prediction

result

Using

LDA

combined

with

PSN

Model

We

use

the

LDA

model

and

combine

it

with

PSN

to

do

the

prediction

which

is

referred

as

LDA-PSN

in

the

paper

Latent

topics

are

detected

by

applying

original

LDA

on

the

training

set

and

LDA

is

used

for

inference

in

testing

data

without

incorporating

new

topics

when

time

passes

by

The

possible

senders

and

receivers

when

new

emails

arrive

-LRB-

-RRB-

new

P

u

r

d

t

is

estimated

as

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

new

old

new

z

P

u

r

d

t

P

u

r

z

t

P

z

d

t

People

are

ranked

by

this

probability

as

the

prediction

results

Using

CTR

Model

People

tend

to

send

emails

to

different

group

of

people

under

different

topics

during

different

time

periods

This

is

the

assumption

we

made

for

our

predictive

model

based

on

CTR

LDA

AT

ART

u

u

LDA

AT

ART

u

u

LDA

AT

ART

u

u

observations

A

N

D

T

u

z

w

r

CTR

S

Tm

t

observations

A

N

D

T

u

z

w

r

CTR

S

Tm

t

Figure

The

graphical

model

for

the

CTR

model

comparing

to

LDA

AT

and

ART

models

where

u

sender

t

time

r

receivers

w

words

z

latent

topics

S

social

network

D

number

of

emails

N

number

of

words

in

one

email

T

number

of

topics

Tm

size

of

the

time

sliding

window

A

number

of

authors

and

are

the

parameters

we

want

to

estimate

with

the

hyperparameters

Industry/Government

Track

Paper

-LRB-

-RRB-

new

P

u

r

d

t

is

estimated

by

applying

the

CTR

model

discussed

in

section

The

prediction

results

are

people

with

highest

scores

calculated

by

equation

-LRB-

-RRB-

and

-LRB-

-RRB-

Using

an

Adaptive

CTR

Model

Both

the

personal

social

network

and

the

CTR

model

ignore

a

key

piece

of

information

from

communication

records

the

dynamical

nature

of

emails

Both

personal

social

network

and

Topic-Community

dynamically

change

and

evolve

Only

based

on

the

training

data

which

are

collected

in

history

will

not

get

the

optimal

performance

for

the

prediction

task

Adaptive

prediction

by

updating

the

model

with

newest

user

behavior

information

is

necessary

We

apply

several

strategies

for

the

adaptive

prediction

The

first

strategy

is

aggregative

updating

the

model

by

adding

new

user

behavior

information

including

the

senders

and

receivers

into

the

model

Then

the

model

becomes

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

i

t

t

i

old

K

i

k

old

k

i

old

t

i

k

z

z

P

u

r

d

t

P

u

r

z

t

P

z

d

t

P

u

r

t

P

z

d

t

-LRB-

-RRB-

where

K

is

the

number

of

old

topics

Here

we

always

use

the

data

from

old

t

including

t

to

i

t

to

predict

the

user

behavior

during

i

t

In

the

second

strategy

we

assume

the

correlation

between

current

data

and

the

previous

data

decays

over

time

The

more

recent

data

are

more

important

Thus

a

sliding

window

of

size

n

is

used

to

choose

the

data

for

building

the

prediction

model

in

which

the

prediction

is

only

dependent

on

the

recent

data

with

the

influence

of

old

data

ignored

Here

in

equation

-LRB-

-RRB-

old

t

consists

of

i

n

t

to

i

t

CommunityNet

Model

We

then

build

a

CommunityNet

model

based

on

the

CTR

algorithm

The

CommunityNet

model

which

refers

to

the

personal

Topic-Community

Network

draws

upon

the

strengths

of

the

topic

model

and

the

social

network

as

well

as

the

dynamic

model

using

a

topic-based

representation

to

model

the

content

of

the

document

the

interests

of

the

users

the

correlation

of

the

users

and

the

receivers

and

all

these

relationship

changing

over

time

For

prediction

CommunityNet

incorporates

the

adaptive

CTR

model

as

described

in

Section

The

first

part

of

our

analysis

focuses

on

identifying

clusters

of

topics

and

the

senders

and

receivers

who

participated

in

those

topics

First

we

analyze

the

topics

detected

from

the

Enron

Corpus

Then

we

study

the

topic-community

patterns

Topic

Analysis

In

the

experiment

we

applied

Bayesian

model

selection

-LSB-

-RSB-

to

choose

the

number

of

topics

In

the

Enron

intra-organization

emails

there

are

word-terms

involved

after

we

apply

stop-words

removal

and

stemming

We

computed

-LRB-

-RRB-

P

w

T

for

T

values

of

topics

and

chose

T

with

the

maximum

value

of

-LRB-

-RRB-

-LRB-

-RRB-

log

P

w

T

for

the

experiment

Topic

Distribution

After

topic

clustering

based

on

words

for

each

document

we

have

P

-LRB-

z

d

-RRB-

which

indicates

how

likely

each

document

belongs

to

each

topic

By

summing

up

this

probability

for

all

the

documents

we

get

the

topic

distribution

of

how

likely

each

topic

occurs

in

this

corpus

We

define

this

summed

likelihood

as

Popularity

of

the

topic

in

the

dataset

From

this

topic

distribution

we

can

see

that

some

topics

are

hot

people

frequently

communicate

with

each

other

about

them

while

some

others

are

cold

with

only

few

emails

related

to

them

Table

illustrates

the

top

topics

in

Enron

corpus

We

can

see

that

most

of

them

are

talking

about

regular

issues

in

the

company

like

meeting

deal

and

document

Table

illustrates

the

bottom

topics

in

Enron

corpus

Most

of

them

are

specific

and

sensitive

topics

like

Stock

or

Market

People

may

feel

less

comfortable

to

talk

about

them

broadly

Table

Hot

Topics

meeting

deal

Petroleum

Texas

document

meeting

plan

conference

balance

presentation

discussion

deal

desk

book

bill

group

explore

Petroleum

research

dear

photo

Enron

station

Houston

Texas

Enron

north

America

street

letter

draft

attach

comment

review

mark

Table

Cold

Topics

Trade

stock

network

Project

Market

trade

London

bank

name

Mexico

conserve

Stock

earn

company

share

price

new

network

world

user

save

secure

system

Court

state

India

server

project

govern

call

market

week

trade

description

respond

Topic

Trend

Analysis

To

sense

the

trend

of

the

topics

over

time

we

calculate

the

topic

popularity

for

year

and

and

calculate

the

correlation

coefficients

of

these

two

series

For

some

topics

the

trends

over

years

are

similar

Figure

-LRB-

a

-RRB-

illustrates

the

trends

for

two

topics

which

have

largest

correlation

coefficients

between

two

years

Topic

which

is

talking

about

a

schedule

issue

reaches

a

peak

during

June

to

September

For

topic

it

is

talking

about

a

meeting

issue

The

trend

repeats

year

to

year

Figure

-LRB-

b

-RRB-

illustrates

the

trend

of

Topic

California

Power

over

to

We

can

see

that

it

reaches

a

peak

from

the

end

of

year

to

the

beginning

of

year

From

the

timeline

of

Enron

-LSB-

-RSB-

we

found

that

California

Energy

Crisis

occurred

at

exactly

this

time

period

Among

the

key

people

related

to

this

topic

Jeff

Dasovich

was

an

Enron

government

relations

executive

His

boss

James

Steffes

was

Vice

President

of

Government

Affairs

Richard

Schapiro

was

Vice

President

of

Regulatory

Affairs

Richard

Sanders

was

Vice

President

and

Assistant

General

Counsel

Steven

Kean

was

Executive

Vice

President

and

Chief

of

Staff

Vincent

Kaminski

was

a

Ph.D.

economist

and

Head

of

Research

for

Enron

Corp.

Mary

Han

was

a

lawyer

at

Enron

's

West

Coast

trading

hub

From

the

timeline

we

found

all

these

people

except

Vince

were

very

active

in

this

event

We

will

further

analyze

their

roles

in

Section

Industry/Government

Track

Paper

Topic

Trend

Comparison

Jan

Mar

May

Jul

Sep

Nov

Po

p

u

l

a

r

i

t

y

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

a

-RRB-

Trends

of

two

yearly

repeating

events

Topic

Analysis

for

Topic

P

opul

ar

i

t

y

Keywords

with

-LRB-

-RRB-

P

w

z

power

California

electrical

price

energy

generator

market

until

Key

people

with

-LRB-

-RRB-

P

u

z

-LRB-

b

-RRB-

The

trend

of

California

Power

and

most

related

keywords

and

people

Figure

Topic

trends

Predicting

Community

Patterns

We

assume

that

people

communicate

with

certain

people

only

under

certain

few

topics

People

in

the

same

community

under

a

topic

would

share

the

information

Thus

if

there

is

something

new

about

one

topic

people

in

that

topic-community

will

most

likely

get

the

information

and

propagate

it

to

others

in

the

community

Finally

many

people

in

the

community

will

get

the

information

To

evaluate

our

assumption

and

answer

the

question

of

who

will

be

possibly

involved

in

an

observed

email

we

collect

the

ground

truth

about

who

are

the

senders

and

receivers

for

the

emails

and

use

the

CTR

algorithm

to

infer

-LRB-

-RRB-

j

new

P

u

r

z

t

by

-LRB-

-RRB-

j

old

P

u

r

z

t

We

partitioned

the

data

into

training

set

and

testing

set

We

tried

two

strategies

for

this

experiment

First

is

to

randomly

partition

the

data

into

a

training

set

with

messages

and

a

testing

set

with

messages

Prediction

accuracy

is

calculated

by

comparing

the

inference

results

and

the

ground

truth

-LRB-

i.e.

receiver-sender

pair

of

that

email

-RRB-

We

found

that

people

stick

in

the

old

topics

they

are

familiar

with

The

second

strategy

is

to

partition

data

by

time

emails

before

as

the

training

data

-LRB-

-RRB-

and

after

that

as

the

testing

data

-LRB-

-RRB-

We

found

of

the

people

keep

their

old

topics

Both

results

are

quite

promising

It

is

found

that

people

really

stick

in

old

topics

they

are

familiar

with

In

this

section

we

evaluate

the

performance

of

CommunityNet

First

we

show

how

people

's

roles

in

an

event

can

be

inferred

by

CommunityNet

Then

we

show

the

predicting

capability

of

the

proposed

model

in

experiments

Role

Discovery

People

with

specific

roles

at

company

hierarchy

behave

specifically

on

specific

topics

Here

we

show

it

is

possible

to

infer

people

's

roles

by

using

CommunityNet

In

Section

we

show

there

are

some

key

people

involved

in

California

Energy

Crisis

In

reality

Dasovich

Steffes

Schapiro

Sanders

and

Kean

were

in

charge

of

government

affairs

Their

roles

were

to

solve

the

problem

Mary

Hain

was

a

lawyer

during

the

worst

of

the

crisis

and

attended

meetings

with

key

insiders

We

calculated

the

correlation

coefficients

of

the

trends

of

these

people

and

the

overall

trend

of

this

topic

Jeff

Dasovich

got

James

Steffes

got

Mary

Hain

got

Richard

Shapiro

got

Steven

Kean

got

-LRB-

all

among

the

highest

correlation

scores

among

people

-RRB-

and

Richard

Sanders

got

-LRB-

ranked

-RRB-

while

Vince

Kaminski

had

correlation

coefficient

of

-LRB-

Figure

-RRB-

We

can

see

that

all

the

key

people

except

Vince

Kaminski

have

strong

correlation

with

the

overall

trend

of

California

Energy

Crisis

From

their

positions

we

can

see

that

all

of

them

were

sort

of

politicians

while

Vince

Kaminski

is

a

researcher

Thus

it

is

clear

to

see

the

difference

of

their

roles

in

this

topic

Po

p

u

l

a

r

i

t

y

Overall

trend

Figure

Personal

topic

trend

comparison

on

California

Power

Predicting

Receivers

Here

we

want

to

address

the

problem

of

whether

it

is

possible

to

infer

who

will

possibly

be

the

receivers

by

a

person

's

own

historic

communication

records

and

the

content

of

the

email-to

send

One

possible

application

is

to

help

people

organize

personal

social

capital

For

instance

if

a

user

has

some

information

to

send

or

a

question

to

ask

CommunityNet

can

recommend

the

right

persons

to

send

the

info

or

get

the

answer

We

conduct

experiments

by

partitioning

the

dataset

into

a

training

set

with

the

emails

from

to

and

a

testing

set

with

the

emails

from

to

The

testing

set

is

further

partitioned

into

sub-sets

with

emails

from

one

month

as

a

subset

With

this

we

have

testing

sets

-LRB-

We

exclude

the

emails

after

March

because

the

total

number

of

emails

after

that

is

only

-RRB-

One

issue

we

want

to

mention

is

that

the

number

of

people

from

to

is

while

from

to

is

In

this

study

we

test

each

email

in

the

training

set

by

using

its

content

Industry/Government

Track

Paper

sender

and

time

as

prior

information

to

predict

the

receiver

which

is

compared

to

the

real

receiver

of

that

email

In

Figure

we

illustrate

the

prediction

performance

by

comparing

the

CTR

algorithm

PSN

and

the

aggregated

LDA-PSN

model

The

result

shows

that

CTR

beats

PSN

by

on

accuracy

The

aggregated

LDA-PSN

model

performs

even

worse

than

PSN

because

of

the

inaccurate

clustering

results

The

performance

gain

is

Moreover

intuitively

personal

contacts

evolve

over

time

Models

built

at

a

specific

time

should

have

decreasing

predicting

capability

over

time

In

this

figure

we

obtain

strong

evidence

of

this

hypothesis

by

observing

that

the

performance

of

these

models

monotonically

decays

This

also

implies

our

models

well

match

the

practice

A

c

cu

r

a

cy

by

PSN

by

CTR

by

LDA-PSN

-LRB-

a

-RRB-

Accuracy

based

on

the

top

most

likely

people

A

c

cu

r

a

cy

by

PSN

by

CTR

by

LDA-PSN

-LRB-

b

-RRB-

Accuracy

based

on

the

top

most

likely

people

Figure

Prediction

Accuracy

comparisons

Accuracy

is

measured

by

testing

whether

the

real

receiver

is

among

the

prediction

list

of

the

top

or

most

likely

people

Inferring

Senders

We

test

whether

it

is

possible

to

infer

who

will

possibly

be

the

senders

given

a

person

's

CommunityNet

and

the

content

of

the

email

One

possible

application

is

to

exclude

spam

emails

or

detect

identification

forgery

Figure

illustrates

the

prediction

result

which

also

shows

the

prediction

accuracy

decays

over

time

A

ccu

r

a

c

y

Figure

Predicting

senders

given

receiver

and

content

Adaptive

Prediction

We

observed

the

prediction

performance

decays

over

time

from

the

results

of

and

which

reflects

the

changes

of

the

nature

of

email

streams

Here

we

apply

adaptive

prediction

algorithms

we

mentioned

in

in

which

we

incrementally

and

adaptively

estimate

statistical

parameters

of

the

model

by

gradually

forgetting

out-of-state

statistics

Ac

c

u

r

a

c

y

Adaptive

CT

R

-LRB-

T

op

-RRB-

Adaptive

CT

R

-LRB-

T

op

-RRB-

CT

R

-LRB-

T

-RRB-

CT

R

-LRB-

T

-RRB-

-LRB-

a

-RRB-

Comparison

between

Adaptive

CTR

and

CTR

models

Adaptive

CTR

-LRB-

aggregative

-RRB-

Adaptive

CTR

-LRB-

months

-RRB-

CTR

LDA-PSN

PSN

-LRB-

b

-RRB-

Comparison

of

algorithms

using

Breese

evaluation

metrics

Figure

Performance

evaluation

for

adaptive

prediction

algorithm

and

overall

comparison

Figure

-LRB-

a

-RRB-

illustrates

the

performance

of

the

Adaptive

CTR

algorithm

and

compares

it

to

the

CTR

algorithm

For

the

data

far

away

from

the

training

data

the

improvement

is

more

than

And

if

we

compare

it

to

the

PSN

and

LDA-PSN

algorithms

the

performance

gains

are

and

respectively

Evaluation

by

this

accuracy

metric

tells

us

how

related

the

top

people

ranked

in

the

prediction

results

are

To

understand

the

overall

performance

of

the

ranked

prediction

results

we

apply

the

evaluation

metric

proposed

by

Breese

-LSB-

-RSB-

and

illustrate

the

overall

comparison

in

Figure

-LRB-

b

-RRB-

This

metric

is

an

aggregation

of

the

accuracy

measurements

in

various

top-n

retrievals

in

the

ranked

list

Among

all

predictive

algorithms

adaptive

CTR

models

perform

best

and

PSN

performs

worst

In

adaptive

CTR

models

estimating

from

recent

data

of

six

months

beats

aggregative

updating

the

model

from

all

the

data

from

the

history

In

this

section

we

show

two

application

systems

we

built

based

on

the

CommunityNet

The

first

one

is

a

visualization

and

query

tool

to

demonstrate

informal

networks

incorporation

The

second

one

is

a

receiver

recommendation

tool

which

can

be

used

in

popular

email

systems

These

demos

can

be

accessed

from

Sensing

Informal

Networks

Personal

Social

Network

Figure

illustrates

the

interface

of

a

visualization

and

query

system

of

CommunityNet

The

distance

of

nodes

represents

the

closeness

-LRB-

measured

by

the

communication

frequencies

-RRB-

of

a

person

to

the

center

person

Users

can

click

on

the

node

to

link

to

Industry/Government

Track

Paper

the

CommunityNet

of

another

person

This

system

can

show

personal

social

networks

which

includes

all

the

people

a

user

contacts

with

during

a

certain

time

period

For

instance

Figure

-LRB-

a

-RRB-

illustrates

the

personal

social

network

of

Vice

President

John

Arnold

from

January

to

December

During

this

period

there

were

people

he

sent

emails

to

regardless

what

they

were

talking

about

An

evolutionary

personal

social

network

is

illustrated

in

Figure

-LRB-

b

-RRB-

in

which

we

show

people

's

personal

social

network

changes

over

time

From

Jan.

to

Dec.

no

new

contact

was

added

to

John

's

PSN

However

people

's

relationship

changed

in

A

Personal

Social

Network

Information

Flow

is

illustrated

in

Figure

-LRB-

c

-RRB-

in

which

we

show

how

the

information

flows

through

the

network

-LRB-

here

we

illustrate

the

information

in

two

levels

-RRB-

-LRB-

a

-RRB-

Personal

Social

Network

of

John

Arnold

-LRB-

-RRB-

Jan

to

Dec

-LRB-

-RRB-

Jan

to

Jun

-LRB-

-RRB-

Jul

to

Dec

-LRB-

b

-RRB-

Evolutionary

Personal

Social

Network

-LRB-

c

-RRB-

Personal

Social

Network

Information

Flow

with

two-level

personal

social

network

of

John

Arnold

Figure

Personal

social

networks

of

John

Arnold

Personal

Topic-Community

Network

Personal

topic-community

network

can

show

whom

this

user

will

contact

with

under

a

certain

topic

On

retrieval

keywords

are

required

for

inferring

the

related

topics

Figure

illustrates

several

personal

topic-community

networks

for

John

Arnold

First

we

type

in

Christmas

as

the

keyword

CommunityNet

infers

it

as

holiday

celebration

and

shows

the

four

people

John

contacted

with

about

this

topic

About

Stock

we

find

John

talked

with

five

people

on

Stock

Market

and

Company

Share

from

Jan.

to

Dec.

Personal

Topic-Community

network

can

be

depicted

by

the

system

too

Figure

Personal

Topic-Community

Networks

when

we

type

in

Christmas

and

Stock

Personal

Social

Capital

Management

Receiver

Recommendation

Demo

When

a

user

has

some

questions

he/she

may

want

to

know

whom

to

ask

how

to

find

an

expert

and

who

may

tell

him/her

more

details

because

of

their

close

relationships

In

our

second

demo

we

show

a

CommunityNet

application

which

addresses

this

problem

This

tool

can

be

incorporated

with

general

email

systems

to

help

users

organize

their

personal

social

capitals

First

after

a

user

login

a

webmail

system

he

can

type

in

content

and/or

subject

then

click

on

the

Show

Content

Topic-Community

This

tool

shall

recommend

appropriate

people

to

send

this

email

to

based

on

the

learned

personal

social

network

or

personal

topic-community

The

distances

of

nodes

represent

the

closeness

of

the

people

to

the

user

Users

can

click

on

the

node

to

select

an

appropriate

person

to

send

email

to

If

the

center

node

is

clicked

then

a

sphere

grows

to

represent

his

ties

to

a

group

of

experts

Click

on

Mail

To

then

the

people

in

the

sphere

will

be

included

in

the

sender

list

In

the

examples

in

Figure

we

log

in

as

Jeff

Dasovich

He

can

ask

his

closest

friends

whenever

he

has

questions

or

wants

to

disseminate

information

If

he

wants

to

inform

or

get

informed

on

Industry/Government

Track

Paper

Government

related

topics

the

system

will

suggest

him

to

send

emails

to

Steffes

Allen

Hain

or

Scott

The

topics

are

inferred

by

matching

the

terms

from

the

Subject

as

well

as

the

content

of

the

email

He

can

also

type

in

Can

you

tell

me

the

current

stock

price

as

the

email

content

This

system

will

detect

Stock

Market

as

the

most

relevant

topic

Based

on

Dasovich

's

CommunityNet

it

shows

three

possible

contacts

He

then

chooses

appropriate

contact

-LRB-

s

-RRB-

-LRB-

a

-RRB-

Receiver

recommendation

for

Government

-LRB-

b

-RRB-

Receiver

recommendation

for

Can

you

tell

me

the

current

stock

price

Figure

Receiver

recommendation

demo

system

In

this

paper

we

propose

a

new

way

to

automatically

model

and

predict

human

behavior

of

receiving

and

disseminating

information

We

establish

personal

CommunityNet

profiles

based

on

a

novel

Content-Time-Relation

algorithm

which

incorporates

contact

content

and

time

information

simultaneously

from

personal

communication

CommunityNet

can

model

and

predict

the

community

behavior

as

well

as

personal

behavior

Many

interesting

results

are

explored

such

as

finding

the

most

important

employees

in

events

predicting

senders

and

receivers

of

emails

etc.

Our

experiments

show

that

this

multi-modality

algorithm

performs

better

than

both

the

social

network-based

predictions

and

the

content-based

predictions

Ongoing

work

includes

studying

the

response

time

of

each

individual

to

emails

from

different

people

to

further

analyze

user

's

behavior

and

also

incorporating

nonparametric

Bayesian

methods

such

as

hierarchical

LDA

with

contact

and

time

information

We

would

like

to

thank

D.

Blei

T.

Griffiths

Yi

Wu

and

anonymous

reviewers

for

valuable

discussions

and

comments

This

work

was

supported

by

funds

from

NEC

Labs

America

-LSB-

-RSB-

B.

A.

Nardi

S.

Whittaker

and

H.

Schwarz

It

's

not

what

you

know

it

's

who

you

know

work

in

the

information

age

First

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-LSB-

-RSB-

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Krackhardt

Panel

on

Informal

Networks

within

Formal

Organizations

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Intl.

Social

Network

Conf.

Feb.

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and

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personal information management
user behavior modeling