Web 2.0 Social Data Analysis

Transcription

1 Web 2.0 Social Data Analysis Ing. Jaroslav Kuchař Structure(1) Czech Technical University in Prague, Faculty of Information Technologies Software and Web Engineering

2 2 Contents Overview Graphs Strong and Weak Ties

3 3 Overview Wayne Zachary. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4): , 1977.

4 4 Applications Businesses analyze and improve communication flow in organization, partners, customers Law enforcement agencies (army) identify criminal and terrorist networks, key players Web Sites identify and recommend potential friends Organizations uncover conflicts of interests (government, lobbies, businesses)

5 5 Network datasets Collaboration Graphs - who works with whom Who-talks-to-Whom Graphs IM, call graphs Information Linkage Graphs pages and links, citations Technological Networks computers, power grid Networks in the Natural World food webs, cascading extinctions, neural connections

6 6 Graphs A graph is a way of specifying relationships among a collection of items. Objects Nodes Edges Directed Undirected Mathematical models and network structures

7 Arpanet in December

8 Transit Schematics 8

9 9 Graph Theory terminological jungle, in which any newcomer may plant a tree John A. Barnes. Social Networks. Number 26 in Modules in Anthropology. Addison Wesley, Path sequence of nodes connected by edges Cycles - path with at least three edges, first and last nodes are the same Connectivity if exists path between nodes

10 10 Graph Theory Components group of nodes, which are connected every node has a path to every other the group is not part of some larger group Giant Components - connected component that contains a significant fraction of all the nodes

11 The collaboration graph of the biological research center Structural Genomics of Pathogenic Protozoa (SGPP) Shawn M. Douglas, Gaetano T. Montelione, and Mark Gerstein. PubNet: a flexible system for visualizing literature derived networks. Genome Biology, 6(9),

12 American High School 18 month period Peter Bearman, James Moody, and Katherine Stovel. Chains of affection: The structure of adolescent romantic and sexual networks. American Journal of Sociology, 110(1):44 99,

13 13 Graph Theory Length - number of edges in the sequence Distance shortest path between nodes Breadth-first search

14 14 The Small-World Phenomenon The idea that the world looks small How short a path of friends it takes to get from you to almost anyone else Six degrees of separation I read somewhere that everybody on this planet is separated by only six other people. Six degrees of separation between us and everyone else on this planet. John Guare. Six Degrees of Separation: A Play. Vintage Books, 1990.

15 15 First experimental study Stanley Milgram 1960s $680 budget 296 random starters Forward letter to target person Stockbroker who lived in a suburb of Boston Given personal information about target Forward to someone the knew Same instructions

16 16 Instant Messaging The distribution of distances in the graph of all active Microsoft Instant Messenger user accounts, with an edge joining two users if they communicated at least once during a month-long observation period 240 million active user accounts average distance of 6.6

17 17 Paul Erdös Itinerant mathematician 1500 papers Erdös number the distance from him or her to Erdös 4 or 5 Albert Einstein 2 Enrico Fermi 3 James Watson - 6

18 18 Kevin Bacon Movie actors and actresses His or her distance in this graph to Kevin Bacon Average = 2.9 I found an incredibly obscure 1928 Soviet pirate film, Plenniki Morya, starring P. Savin with a Bacon number of 7, and whose supporting cast of 8 appeared nowhere else

19 19 Strong and Weak Ties how information flows through a social network how different nodes can play structurally distinct roles shape the evolution of the network Weight of Edge: Frequency of interaction, number of exchanged items, strength of relationship

20 20 Triadic Closure If two people in a social network have a friend in common, then there is an increased likelihood that they will become friends themselves at some point in the future Anatole Rapoport. Spread of information through a population with socio-structural bias I: Assumption of transitivity. Bulletin of Mathematical Biophysics, 15(4): , December 1953.

21 21 The Clustering Coefficient simple social network measure probability that two randomly selected friends are friends with each other fraction of pairs of friends that are connected to each other by edges

22 22 The Clustering Coefficient A coefficient = 1/6 only C-D from all possible (C-D,D-E, B-E, B-C,C-E,D-B) A coefficient = 2/6 = 1/3 C-D and B-C from all possible (C-D,D-E, B-E, B-C,C-E,D-B)

23 23 Bridge and Local Bridge Bridge Local Bridge

24 24 Neighborhood overlap Helps identify local bridges Edge between A and B Local Bridge -> NO = 0 NO A-B # nodes who are neighbors of both A and B # nodes who are neighbors of at least one A and B

25 25 Neighborhood overlap Edge A-F NO = 1/6 Edge A-B NO = 0/8

26 Different node plays different role 26

27 27 Embeddedness Embeddedness of edge number of common neighbors the two endpoints have A-B = 2 Common E and F Local bridges has embeddedness = 0 Signifficant embeddedness -> easier trust, confidence (social, economic)

28 28 Structural hole Node B Access information from non-interacting parts Interface Novel ideas Gatekeeping

29 Questions 29

30 30 Resources D. Easley, J. Kleinberg: Networks, Crowds and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010, ISBN