PhD Research Proficiency Exam

Computing & Information SciencesKansas State University

Laboratory forKnowledge Discovery in Databases

PhD Research Proficiency Exam

Jing XiaLaboratory for Knowledge Discovery in Databases

Department of Computing and Information Sciences

Kansas State University

http://www.kddresearch.org

http://www.cis.ksu.edu/~xiajing

Social Network Analysis using Link Mining



Social Network Introduction

Networks in Biological System

Mining on Social NetworkLinking MiningMulti Relational Mining

Problem Specification

Proposed approach

OutlineOutline



Social Network IntroductionSocial Network Introduction

What is Social Network?a social net work is a heterogeneous and

multirelational data set represented by a graph.

Characteristics of Social Network“Natural” Networks and UniversalityQuantitative measures

Mining Social NetworkLink Mining: Tasks and Challenges



Society Society

Nodes: individuals

Links: social relationship(family/work/friendship/etc.)

S. Milgram (1967) “natural” network appears to be a universal Six Degrees of Separation

Society networks: Many individuals with diverse social interactions between them.

2023年4月20日 Data Mining: Concepts and Techniques4



CommunicationCommunication

The Earth is developing an electronic system, a network with diverse nodes and links are

-computers

-routers

-satellites

-phone lines

-TV cables

-EM waves

Communication networks: Many non-identical components with diverse connections between them.



EpidemiologyEpidemiology

Nodes: doctors, patients, geological location

Links: contact relationship(direct/indirect infectiousness)



Characteristics of Social NetworkCharacteristics of Social Network

Consider many kinds of networks:social, technological, business, economic, content,…

These networks tend to share certain informal properties:Multi relational interactionTemporal (time-evolving)large scale; continual growthdistributed, organic growth: vertices “decide” who to

link tomixture of local and long-distance connectionsabstract notions of distance: geographical, content,

social,…



Social Network TheorySocial Network Theory

Do natural networks share more quantitative universals?

What would these “universals” be?How can we make them precise and measure them?How can we explain their universality?

This is the domain of social network theorySometimes also referred to as link analysis



Quantitative MeasureQuantitative Measure

Connected components:how many, and how large?

Network diameter:maximum (worst-case) or average?exclude infinite distances? (disconnected

components)the small-world phenomenon



Quantitative MeasureQuantitative Measure

Clustering: to what extent that links tend to cluster “locally”? what is the balance between local and long-

distance connections? what roles do the two types of links play?

Degree distribution: what is the typical degree in the network? what is the overall distribution?







OutlineOutline



Bio-MapBio-Map

Protein-gene interaction

protein-protein interactions

PROTEOME

GENOME

Citrate Cycle

METABOLISM

Bio-chemical reactions



Protein-Protein Interaction Network


protein-protein interactions

PROTEOME





Nodes: proteins Links: multi relational

physical interactions (binding)complex membershipPathway

P. Uetz, et al. Nature 403,

623-7 (2000).







Proposed approach

OutlineOutline



Link MiningLink Mining

Traditional machine learning and data mining approaches assume: data is flat

Typical real data setInstances in data set form linked networks

Link Mining

Newly emerging research area at the intersection of research in social network and link analysis, hypertext and web mining, graph mining, relational learning and inductive logic programming



Link Mining TasksLink Mining Tasks

Object-Related TasksLink-based object rankingLink-based object classificationObject clustering (group detection)Object identification (entity resolution)

Link-Related TasksLink prediction

Graph-Related TasksSubgraph discoveryGraph classification



Multi-relational Link MiningMulti-relational Link Mining

Traditional link mining assume there is only one kind of relation in the network: link is flat

There exist multiple, heterogeneous social networks, each representing a particular kind of relationshipMulti-relational & heterogeneous



Multi-relational NetworkMulti-relational Network

Multi-relational & heterogeneous NetworkMultiple object and link types

Example NetworkMedical network: patients, doctors, disease,

contacts, treatmentsBibliographic network: years, publications, authors,

venuesEpidemic transmission network (involve temporal

data, multi-relational: airborne, patients’ contacts







Proposed approach

OutlineOutline



Problem SpecificationProblem Specification

Phenomenon: Heterogeneity & Multi-relationship exists in many real network

Rationale: it might be useful for link mining

ProblemCan we utilize multi-relationship to help

link analysisHow to extract relations as relation network (RN)?How to identify relationship among relation

network? (co-relation, independent, etc)Is RN time-evolving? Which relation plays an

important role?



Problem Example1Problem Example1

Application Domain: Epidemic Disease

Pre-condition 1: given multi relations -- patients’ contacts network in timeline

Pre-condition 2: sequential relationship among relations

Pre-condition 3: another medium of disease transmission

Problem: can we predict if any person will be infected, based on mining these multi-relational networks?




Application Domain: bibliographic network

Pre-condition 1: given multi relations – the co-author relation networks of a conference in some years

Problem 1: what is the relationship among these relation networks

Problem 2: How can we utilize the relationship to meet the user’s query

Mining Hidden Community in Heterogeneous Social Networks, Deng Cai, Zheng Shao, Xiaofei He, Xifeng Yan, and Jiawei Han, March, Report No. UIUCDCS-R-2005-2538 UILU-ENG-2005-1731




Application Domain: bibliographic network

Pre-condition 1: given multi relations – the co-author networks of a conference in some years

Pre-condition 2: topics of publications

Problem: Can we predict if two researchers will be co-author in the future, based on two types of networks?







Proposed approach

OutlineOutline



Proposed approachProposed approach

Random walk with restart

1

4

3

2

5 6

7

9 10

811

12

Node 4

Node 1Node 2Node 3Node 4Node 5Node 6Node 7Node 8Node 9Node 10Node 11Node 12

0.130.100.130.220.130.050.050.080.040.030.040.02

1

43

2

5 6

7

9 10

811

120.13

0.10

0.13

0.130.05

0.05

0.08

0.04

0.02

0.04

0.03

More red, more relevant

Nearby nodes, higher scores



Proposed approachProposed approach

Basic ideaRWR serves as a measure for proximity between

two nodes in networkModel relationship among multi relations using

RWR

PurposeFacilitate mining more interesting patternsIncrease prediction accuracy



Measure Relationship Measure Relationship

ICDM

KDD

SDM

ECML

PKDD

PAKDD

CIKM

DMKD

SIGMOD

ICML

ICDE

0.009

0.011

0.0080.007

0.005

0.005

0.005

0.0040.004

0.004

A: RWR!

Q: what is most related conference to ICDM

Neighborhood Formulation [Sun ICDM2005]



Multi-Relational ModelMulti-Relational Model

ICDM author network

KDD author network

PKDD author network ICML author network

ICDM

KDD

SDM

ECML

PKDD

PAKDD

CIKM

DMKD

SIGMOD

ICML

ICDE

0.009

0.011

0.0080.007

0.005

0.005

0.005

0.0040.004

0.004

relation network



Other ApplicationsOther Applications

Content-based Image Retrieval [He]

Personalized PageRank [Jeh], [Widom], [Haveliwala]

Anomaly Detection (for node; link) [Sun]

Link Prediction [Getoor], [Jensen]

Semi-supervised Learning [Zhu], [Zhou]…



Social Network Analysis

Linking mining

Problem: multi relational

Proposed approach

SummarySummary



Thank youThank you

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PhD Research Proficiency Exam