On the Social Network Analysis and Mining

On the Social Network

Analysis and Mining:

A Brief Introduction

Leon S.L. Wang (王學亮)

Department of Information Management National University of Kaohsiung

Outline

• What is Social Network?

• Social Networks Analysis

• Social Network Extraction &

Construction

• Social Network Applications

• Challenges in Mining Social

Introduction (1)

• What is Social Network?

– A social network is a social structure to

describe social relations (wikipedia)

– The history of Social Network is older

than everybody who is here

• More than 100 years (Cooley 1909, Durkheim 1893)

• Focusing on small groups

– Information Techniques give it a new life

– From Sociology to Computer Science

Introduction (2)

• Topics about Social Networking

– Social Networking: Analyzing and

Constructing Social Network

• Social Network Extraction and Construction

• Social Network Analysis

• Online Social Networking

Social Network Analysis (1)

• Social Network Analysis

– A simple social network diagram

• Roles

• Relationships

– One way – Two way

– Positive & Negative

– Self-defined relationships

• Visualization

– Why visualization?

» Providing as much information as possible in a social network

» Human can easily and roughly

Social Network Analysis (2)

• Relational Data

Companies-by-companies 1 2 3 4 1 - 3 3 1 2 3 - 2 2 3 3 2 - 1 4 1 2 1 -Adjacency matrix: directors-by-directors A B C D E A - 2 2 1 1 B 2 - 3 2 1 Directors A B C D E Com pan ies 1 1 1 1 1 0 2 1 1 1 0 1 3 0 1 1 1 0 4 0 0 1 0 1 1 2 4 3 1 1 2 2 3 3 A B 1 1 1 2 2 2 3

Social Network Analysis (3)-1

• Measurement

– Size

• Density

• Geodesic distance

• Diameter

• Closeness

– Centrality

• Degree

• Betweenness

• Closeness

Social Network Analysis (3)-2

• Density Measurement-An example

Connected Points 4 4 4 3 2 0 Inclusiveness 1.0 1.0 1.0 0.7 0.5 0 Sum of degrees 12 8 6 4 2 0 No. of lines 6 4 3 2 1 0

Social Network Analysis (3)-3

• Centrality

– Degree, Betweenness, Closeness, Eigenvalue

Social Network Analysis (3)-4

• Measurement

– Clustering Coefficient

– Path Length, Trail, Walk

– Reachability

– Structural Hole

– Reciprocity

– K-Clique

– Position

Social Network Analysis (3)-5

• Measurement

– Clustering Coefficient

• Local

• Global

Social Network Analysis (3)-6

• Measurement

Social Network Analysis (3)-7

• Measurement

– Reciprocity

• the number of ties that are involved in reciprocal

relations relative to the total number of actual ties

• |(AB, BA)| / |(AB, BA, AC)|

• = 2/3

Social Network Analysis (3)-8

• Measurement

– Clique

• Complete subgraph

• Maximum clique

• Maximal cliques

• K-clique

Social Network Analysis (5)

• Sociologists only focus on small social

networks

– 50~100 nodes in a social network

• The advent of Internet communications

has greatly increased SNA‟s popularity

– Computer & Information Technologies

become essential tools for SNA

Can You Analyze and Construct this Social Network

Diagram by hand??

Social Networking

• Social Network = Computer Network

– Next Target: Mobile Phone

• Facebook is collaborating with Cingular

Wireless, Sprint Nextel & Verizon Wireless

• Killer Applications are Needed

– E-commerceWallop

– Job Finder

Linkedin

• On-line Social Networking Websites

– Using

people

to find

content

Social Network Extraction & Construction

• Extracting & Constructing Social Networks from

Contents

– Using

content

to find

people

– Contents

• Web

• E-mail

• Event-logs

• On-line Chat

• Papers & Theses

Social Network Extraction & Construction (2)

• Extracting Social Networks from Web

– Extracting from web contents (Personal Homepage)

– Semantic Analysis (Ontology) & NLP (Natural Language Processing) (Jin et al. 2007)

– Contacting information is the focus (Culotta et al. 2005)

• E-mail address • Phone Number • Names – Network Analysis • Appearance • Connectivity • URLs Similarity 19

Social Network Extraction & Construction (3)

• Extracting Social

Networks from E-mail

– A most used on-line

communication application

– E-mail is a semi-structured

document

– Form: „Bill Stoddard‟ <reddrum@attglobal.net>

• Subject • Receiver • Date & Time

Me A B C Me A B C A B

Social Network Extraction & Construction (4)

• Extracting Social Networks from Chat

– Internet Relay Chat (Chat Room)

– Instant Messenger

• MSN Messenger, ICQ, Yahoo! Messenger,……

• MSN messenger provides a XML based and structured communication logs

– Date & Time – Sender

– Receiver – Messages

• Network Analysis

– Communication Frequency & Closeness

– Contact Sharing (Who may also your friends) – Automatic Grouping and Blocking

Social Networking Applications (1)

• Marketing & E-commerce

– Target Marketing

– Collaborative Recommendation

• Terrorist & Crime Detection

– Ipswich‟s Jack the Ripper, England 2006

• Medical Network

– Finding Blood – Organ

• Knowledge Management

Social Networking Applications (2)

• Learning

• Organizational Social Network Analysis

– Optimice

• Politic & Election

• Academic Social Networking

– Family Tree

• Game AI

– On-line Game

– Game with Social Network (Game 2.0)

• Second Life

• And Much More………

Some Link Mining Tasks (1)

• Object-Related Tasks

– Link-based Object Ranking

• PageRank, HITS, Centrality, Tagommender

– Link-based Object Classification

• News items classification, Folksonomy

– Object Clustering (Group Detection)

• Finding positions, a set of people with similar links

– Object identification

• Same name, different people

• Link-Related Tasks

Some Link Mining Tasks (2)

• Graph-Related Tasks

– Subgraph Discovery

• K-Cliques, K-Clans, K-Plexes

– Graph Classification

– Generative Models for Graphs

Challenges in Mining Social

Network Data

Adopted and Modified from the talk of Jon M. Kleinberg

Challenge 1: Splitting Network

Challenge 2: A Matter of Scale

• 436-node network

of e-mail exchange over 3 months at

a corporate research lab (Adamic-Adar 2003)

• 43,553-node network

of e-mail exchange over 2 years at

a large university (Kossinets-Watts 2006)

• 4.4-million-node network

of declared friendships on

blogging community LiveJournal (Liben-Nowell et al.

2005, Backstrom et al. 2006)

• 240-million-node network

of all IM communication over

one month on Microsoft Instant Messenger

(Leskovec-Horvitz‟07)

Challenge 2: A Matter of Scale

• Currently, massive network datasets give

you both more and less:

– More: can observe global phenomena that are

genuine, but literally invisible at smaller

scales.

– Less: Don‟t really know what any one node or

link means. Easy to measure things; hard to

pose nuanced questions.

– Goal: Find the point where the lines of

research converge.

Challenge 3: Geographic Data

• Liben-Nowell, Kumar, Novak, Raghavan, Tomkins (2005) studied • LiveJournal, an on-line blogging community with friendship links • Large-scale social network with geographical embedding:

– 500,000 members with U.S. Zip codes, 4 million links.

Challenge 4: Diffusion in Social Networks

• Diffusion, another fundamental social processs:

Behaviors that cascade from node to node like an

epidemic.

– News, opinions, rumors, fads, urban legends, ... – Viral marketing [Domingos-Richardson 2001]

– Public health (e.g. obesity [Christakis-Fowler 2007]) – Cascading failures in financial markets

Challenge 5: Protecting Privacy in Social Network Data

• Many large datasets based on communication (e-mail, IM, voice) where users have strong privacy expectations.

– Current safeguards based on anonymization: replace node names with random IDs.

• With more detailed data, anonymization has run into

trouble:

– Identifying on-line pseudonyms by textual analysis – De-anonymizing Netflix ratings via time series

– Search engine query logs: identifying users from their queries.

• Does this make things safer?

– E.g. no text, time-stamps, or node attributes

Challenge 6: Attacking an Anonymized Network

• What we learn from this:

• Attacker may have extra power if they are part of the system. In large e-mail/IM network, can easily add yourself to system.

• But “finding yourself” when there are 100 million nodes is going to be more subtle than when there are 34 nodes.

• Template for an active attack on an anonymized network

– Attacker can create (before the data is released) nodes (e.g. by registering an e-mail account) edges incident to these nodes (by sending mail)

– Privacy breach: learning whether there is an edge between two existing nodes in the network.

Challenge 7: Network Abstraction

• It is really difficult just like sampling