An Introduction to Social Network Analysis Yi Li 2012-6-1.

Slides:



Advertisements
Similar presentations
Complex Networks for Representation and Characterization of Images For CS790g Project Bingdong Li 9/23/2009.
Advertisements

Network Matrix and Graph. Network Size Network size – a number of actors (nodes) in a network, usually denoted as k or n Size is critical for the structure.
Leting Wu Xiaowei Ying, Xintao Wu Aidong Lu and Zhi-Hua Zhou PAKDD 2011 Spectral Analysis of k-balanced Signed Graphs 1.
Λ14 Διαδικτυακά Κοινωνικά Δίκτυα και Μέσα Positive and Negative Relationships Chapter 5, from D. Easley and J. Kleinberg book.
CHAPTER 8: AFFILIATION AND OVERLAPPING SUBGROUPS SOCIAL NETWORK ANALYSIS BY WASSERMAN AND FAUST AFFILIATION NETWORKS Adapted from a presentation by Jody.
Relationship Mining Network Analysis Week 5 Video 5.
 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Enabling Networked Knowledge.
Selected Topics in Data Networking
Feb 20, Definition of subgroups Definition of sub-groups: “Cohesive subgroups are subsets of actors among whom there are relatively strong, direct,
Mining and Searching Massive Graphs (Networks)
CS447/ECE453/SE465 Prof. Alencar University of Waterloo 1 CS447/ECE453/SE465 Software Testing Tutorial Winter 2008 Based on the tutorials by Prof. Kontogiannis,
Centrality and Prestige HCC Spring 2005 Wednesday, April 13, 2005 Aliseya Wright.
CSE 222 Systems Programming Graph Theory Basics Dr. Jim Holten.
CSE 321 Discrete Structures Winter 2008 Lecture 25 Graph Theory.
Graphs G = (V,E) V is the vertex set. Vertices are also called nodes and points. E is the edge set. Each edge connects two different vertices. Edges are.
CS8803-NS Network Science Fall 2013
Network Measures Social Media Mining. 2 Measures and Metrics 2 Social Media Mining Network Measures Klout.
Course Overview & Introduction to Social Network Analysis How to analyse social networks?
Sunbelt XXIV, Portorož, Pajek Workshop Vladimir Batagelj Andrej Mrvar Wouter de Nooy.
Information Networks Introduction to networks Lecture 1.
Graph Theoretic Concepts. What is a graph? A set of vertices (or nodes) linked by edges Mathematically, we often write G = (V,E)  V: set of vertices,
Social Network Analysis: A Non- Technical Introduction José Luis Molina Universitat Autònoma de Barcelona
Data Analysis in YouTube. Introduction Social network + a video sharing media – Potential environment to propagate an influence. Friendship network and.
GRAPHS CSE, POSTECH. Chapter 16 covers the following topics Graph terminology: vertex, edge, adjacent, incident, degree, cycle, path, connected component,
Principles of Social Network Analysis. Definition of Social Networks “A social network is a set of actors that may have relationships with one another”
1 ELEC692 Fall 2004 Lecture 1b ELEC692 Lecture 1a Introduction to graph theory and algorithm.
Introduction to Graphs. Introduction Graphs are a generalization of trees –Nodes or verticies –Edges or arcs Two kinds of graphs –Directed –Undirected.
Science: Graph theory and networks Dr Andy Evans.
Murtaza Abbas Asad Ali. NETWORKOLOGY THE SCIENCE OF NETWORKS.
Vertices and Edges Introduction to Graphs and Networks Mills College Spring 2012.
Understanding Crowds’ Migration on the Web Yong Wang Komal Pal Aleksandar Kuzmanovic Northwestern University
Midterm Project Guide Prof. Dr. Daning Hu Department of Informatics University of Zurich Oct 23th, 2012.
GRAPHS THEROY. 2 –Graphs Graph basics and definitions Vertices/nodes, edges, adjacency, incidence Degree, in-degree, out-degree Subgraphs, unions, isomorphism.
Susan O’Shea The Mitchell Centre for Social Network Analysis CCSR/Social Statistics, University of Manchester
Spectral Analysis based on the Adjacency Matrix of Network Data Leting Wu Fall 2009.
Networks Igor Segota Statistical physics presentation.
Data Structures & Algorithms Graphs
Centrality in Social Networks Background: At the individual level, one dimension of position in the network can be captured through centrality. Conceptually,
A project from the Social Media Research Foundation: Finding direction in a sea of connection:
Complex Network Theory – An Introduction Niloy Ganguly.
Slides are modified from Lada Adamic
Introduction to Graph Theory
Graphs & Matrices Todd Cromedy & Bruce Nicometo March 30, 2004.
Complex Network Theory – An Introduction Niloy Ganguly.
Graphs A graphs is an abstract representation of a set of objects, called vertices or nodes, where some pairs of the objects are connected by links, called.
Data Structures & Algorithms Graphs Richard Newman based on book by R. Sedgewick and slides by S. Sahni.
GRAPHS. Graph Graph terminology: vertex, edge, adjacent, incident, degree, cycle, path, connected component, spanning tree Types of graphs: undirected,
Class 2: Graph Theory IST402. Can one walk across the seven bridges and never cross the same bridge twice? Network Science: Graph Theory THE BRIDGES OF.
Copyright © Curt Hill Graphs Definitions and Implementations.
Class 2: Graph Theory IST402.
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
Information Retrieval Search Engine Technology (10) Prof. Dragomir R. Radev.
GUILLOU Frederic. Outline Introduction Motivations The basic recommendation system First phase : semantic similarities Second phase : communities Application.
Selected Topics in Data Networking
Social Networks Analysis
Department of Computer and IT Engineering University of Kurdistan
Comparison of Social Networks by Likhitha Ravi
June 2017 High Density Clusters.
Graphs and Graph Models
SOCIAL NETWORKS Amit Sharma INF -38FQ School of Information
Network Science: A Short Introduction i3 Workshop
Why Social Graphs Are Different Communities Finding Triangles
Definition of Criteria for Impact Analysis
Graphs.
CS 594: Empirical Methods in HCC Social Network Analysis in HCI
SOCIAL NETWORKS Amit Sharma INF -38FQ School of Information
(Social) Networks Analysis II
Graphs G = (V, E) V are the vertices; E are the edges.
GRAPHS G=<V,E> Adjacent vertices Undirected graph
Graphs G = (V,E) V is the vertex set.
Presentation transcript:

An Introduction to Social Network Analysis Yi Li

Source This is a reference book … a comprehensive review of network methods … can be used by researchers who have gathered network data and want to find the most appropriate method by which to analyze them. -- Preface Publish Year: 1994 Cited: (Google Scholar)

Outline Mathematical Preliminaries Methods – Centrality and Prestige – Structural Balance – Cohesive Subgroups Possible Applications in Our Work

Outline Mathematical Preliminaries Methods – Centrality and Prestige – Structural Balance – Cohesive Subgroups Possible Applications in Our Work

Graph Theory

Incidence Matrix for a Graph

Outline Mathematical Preliminaries Methods – Centrality and Prestige – Structural Balance – Cohesive Subgroups Possible Applications in Our Work

Overview Measure the prominence of actors – For undirected graph, measure centrality – For directed graph, measure centrality and prestige Four centrality measures Three prestige measures Measure individuals  Aggregate to groups

What do we mean by “prominent”? An actor is prominent  The actor is most visible to other actors Two kinds of actor prominence / visibility – Centrality To be visible is to be involved – Prestige To be visible is to be targeted Group centralization = How different the actor centralities are (How unequal the actors are)?

Centrality (1): Actor Degree Centrality Degree of n i Max possible degree of an actor (g actors in total) A star graph

Centrality (1): Group Degree Centralization Max actor degree centrality in this graph Group degree difference of a Star graph Group degree difference

Centrality (2): Actor Closeness Centrality Total distances between all others and n i Min possible value of the total distance A star graph

Centrality (2): Group Closeness Centralization

Centrality (3): Actor Betweenness Centrality A star graph

Centrality (3): Group Betweenness Centralization

Centrality (4): Information Centrality

Prestige (1): Degree Prestige The in-degree of actor i

Prestige (2): Proximity Prestige The fraction of i’s influence domain Average distance

Prestige (3): Rank Prestige

Outline Mathematical Preliminaries Methods – Centrality and Prestige – Structural Balance – Cohesive Subgroups Possible Applications in Our Work

What is structural balance?

Cycle Balance (Nondirectional) Attitude between P, O, and X Positive Cycle (Pleasing, Balanced) Negative Cycle (Tension, Not Balanced)

Structural Balance (Nondirectonal) A signed graph is balanced iff all cycles are positive. If a graph has no cycles, its balance is undefined (or vacuously balanced)

Balance: Directional A negative semicycle A signed digraph is balanced iff all semicycles are positive – Semicycles: Cycles that formed by ignoring the direction of edges

Clusterability A signed graph is clusterable if it can be divided into many subsets such that positive lines are only inside subsets and negative lines are only across subsets. Balanced graph has 1 or 2 clusters. Unbalanced graph may have several (surely balanced) clusters. (Separation of Tensions) A Clustering

Check Clusterability A signed (di-)graph is clusterable iff it contains no (semi-)cycles which have exactly one negative line. For a complete signed (di-)graph, the 4 statements are equivalent: – It is clusterable. – It has a unique clustering. – It has no (semi-)cycle with exactly one negative line. – It has no (semi-)cycle of length 3 with exactly one negative line.

Outline Mathematical Preliminaries Methods – Centrality and Prestige – Structural Balance – Cohesive Subgroups Possible Applications in Our Work

Overview Definitions of cohesive subgroups in a graph Measures of subgroup cohesion in a graph Extensions – Digraph – Valued Relation – Two-mode graph

Definitions of a Cohesive Subgroup (CS) Four kinds of ideas to define a CS: Members of a CS would – interact with each other directly – interact with each other easily – interact frequently – interact more frequently compare to non-members

Definition (1/4): Based on Clique

Definition (2/4): Based on Diameter X Y A 2-clique (X and Y are not close inside the clique) (A fragile CS)

Definition (3/4): Based on Degree

Definition (4/4): Based on Inside- Outside Relations

Measure the Subgroup Cohesion

Extension (1/3): Digraph For definition 1: clique for digraph For definition 2 to 4 (all care about connectivity) Use one of these digraph-connectivities: – Weakly connected: a semipath between i and j – Unilaterally connected: a path from either i to j or j to i – Strongly connected: Both paths from i to j and j to i – Recursively connected: i and j are strongly connected, and the forward and backward paths contain the same nodes and arcs

An Example Application: Code to Feature Actor = Class, Function Edge = Call, Reference, … Cohesive Subgroup = Feature Sven Apel, Dirk Beyer. Feature Cohesion in Software Product Lines :An Exploratory Study. ICSE ‘11 Measure the cohesion visually

Extension (2/3): Valued Relation Cohesive Group at Level 2

Extension (3/3): Two-Mode Networks A two-mode network: Two kinds of nodes (actors and events), relations are between different kinds of nodes Represent two-mode networks – Affiliation Matrix – Bipartite Graph – Hypergraph StudentsClubsStudent 1 Student 2 Student 3 Club 1 Club 2 Club 3 Affiliate ACTOREVENT

Idea 1: Convert Two-Mode to One-Mode Convert into 2 graphs: (Similar Actors) Co-membership Valued Graph: i links to j at value C iff Actor i and actor j affiliate C same events. (Similar Events) Overlap Valued Graph: i links to j at value C iff Event i and event j own C same actors. Apply one-mode network analysis methods to these graphs

Idea 2: Consider actors and events together

Example: Input Data

Example: 2-Dimensional Correspondence Analysis Close points have similar profiles.

Outline Mathematical Preliminaries Methods – Centrality and Prestige – Structural Balance – Cohesive Subgroups Possible Applications in Our Work

Our Work: Collaborative Feature Modeling Feature Model (Inner Knowledge) Personal View YPersonal View X CreateSelect ViewDeny Modeling Activities Person X Person Y perform Mash stimulate Directly Affect Indirectly Affect For Personal Use Eco-system Boundary Outter Knowledge Books Documents Codes … An Overview of CoFM Eco-system

Possible Networks in CoFM

THANK YOU!