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SOCIAL NETWORK ANALYSIS

SOCIAL NETWORK ANALYSIS. SAFI JANG RABBIYA IJAZ SHAZA KHAN RANA KASHAN OSAMA MASOOD. WHAT IS SNA ?. A social network analysis examines the structure of social relationships in a group to uncover the informal connections between people.

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SOCIAL NETWORK ANALYSIS

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  1. SOCIAL NETWORK ANALYSIS SAFI JANG RABBIYA IJAZ SHAZA KHAN RANA KASHAN OSAMA MASOOD

  2. WHAT IS SNA ? A social network analysis examines the structure of social relationships in a group to uncover the informal connections between people. It is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities

  3. 3 But SNA is not just a methodology It is a unique perspective on how society functions

  4. BASIC CONCEPTS 4

  5. REPRESENTING NETWORKS 5 NODES are the individual actors within the networks EDGES are the relationships between the actors

  6. 6

  7. IDENTIFYING STRONG/WEAK TIES IN THE NEWTWORK 7 ADD WEIGHTS TO EDGES . WEIGHTS COULD BE : FREQUENCY OF INTERACTION NUMBER OF ITEMS EXCHANGED DISTANCE , ETC

  8. HOW TO IDENTIFY KEY/CENTRAL NODES To understand networks and their participants, we evaluate the location of actors in the network. Measuring the network location is finding the centrality of a node. 8

  9. DEGREE CENTRALITY 9 • It is the number of direct connections a node has. In the network above, Diane has the most direct connections in the network, making her the 'connector' or 'hub' in this network. • For a graph G: = (V,E) with n vertices, the degree centrality Cd(v) for vertex v is:

  10. BETWEENNESS CENTRALITY 10 While Diane has many direct ties, Heather has few direct connections Yet, she has one of the best locations in the network -- she is between two important constituencies. where σst is the number of shortest paths from s to t, and σst(v) is the number of shortest paths from s to t that pass through a vertex v the golden rule of networks is: Location, Location, Location !

  11. CLOSENESS CENTRALITY 11 • Fernando and Garth have fewer connections than Diane, yet the pattern of their direct and indirect ties allow them to access all the nodes in the network more quickly than anyone else. • They are in an excellent position to monitor the information flow in the network !

  12. BOUNDARY SPANNERS Nodes that connect their group to other sub-groups in a network Boundary Spanners are those in a social network who can span across various social networks. They can be essential to the flow of novel information. Boundary spanners can be used by the news media in setting its agenda by getting information and ideas to a variety of social networks, rather than just one. 12

  13. CHARACTERISING NETWORK STRUSTURES Reciprocity The ratio of the number of relations which are reciprocated (i.e. there is an edge in both directions) over the total number of relations in the network Indicator of the degree of mutuality 13

  14. Density A network’s density is the ratio of the number of edges in the network over the total number of possible edges between all pairs of nodes It is a common measure of how well connected a network is 14

  15. APPLICATIONS OF SNA IN THE REAL WORLD In Organizations In Crime Investigation In Health Care In Social Networking Websites In Preventing Terrorism In Fraud Detection 15

  16. SNA In Organizations 16 Through research it has been found out that most of the work done in an organization is through informal communication channels Mapping and analyzing these informal channels helps managers understand how communication actually takes place in an organization

  17. 17 SNA also helps in identifying hidden links and helps in improving the communication flow.

  18. SNA In Social Networking Websites 18 SNA used for better recommendations

  19. SNA In Health Care 19 Information, disease pathogens, ideas, money, and many other things can flow across networks Hidden patterns of genetic disease transferred from one generation to another Diseases transfer from physical contact

  20. SNA In Preventing Terrorism 20 • By mapping the social network of the subjects. • Example • 9/11 • Mumbai Attacks

  21. SNA In Crime Investigation 22 • Very much similar to terrorism • Privacy preserving SNA • When 2 or more agencies are involved • When laws keep you from sharing data • Computation o important metrics while keeping the entire network unknown

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  23. SNA In Fraud Detection 24 Build Data Repository SNA provides top-down and bottom-up analysis for uncovering previously hidden linkages It detects risky networks

  24. SMALL WORLD EXPERIMENT The theory states that everybody on this planet is separated by only six other people. The "Six Degrees" Facebook application calculates the number of steps between any two members 25

  25. SNA TOOLS 26

  26. Pajek 27 • A program package designed for Windows, Pajek is most commonly used to analyze large and complex networks. • Pajek also provides tools for analysis and visualization of: • collaboration networks • organic molecule in chemistry • Internet networks • data-mining (2-mode networks), etc.

  27. Analysis in Pajek 28 Analyses in Pajek are performed using six data structures: Network – main object (vertices and lines - arcs, edges); Partition – nominal property of vertices (gender); Vector – numerical property of vertices; Permutation – reordering of vertices; Cluster – subset of vertices (e.g. a cluster from partition); Hierarchy – hierarchically ordered clusters and vertices.

  28. Using Pajek 29 Some properties of nice pictures of networks: Not too many crossings of lines A graph that can be drawn without crossing of lines is called a planar graph Not too many small angles among lines that have one vertex in common Not too long or too short lines (all lines approximately of the same length) Vertices should not be too close to lines

  29. Using Pajek 30 Two approaches to deal with large networks: • Local view: obtained by extracting subnetwork induced by selected cluster of vertices. • Example: Students in the class: relations among boys (girls) only. • Global view: obtained by shrinking vertices in the same cluster to new (compound) vertex. In this way relations among clusters of vertices are shown. • Example: Students in the class: compound relation between boys and girls (number of arcs between the two groups).

  30. Using Pajek 31

  31. Goals of Pajek 32 The main goals in the design of Pajek are: To support abstraction by (recursive) decomposition of a large network into several smaller networks that can be treated further using more sophisticated methods; To provide the user with some powerful visualization tools; To implement a selection of efficient (subquadratic) algorithms for analysis of large networks.

  32. Twitter & SNA 33 Tweetwheel: Allows you to view and analyze relationships within a network. Below is a screenshot taken of the analysis of a Twitter Network.

  33. Facebook & SNA: TouchGraph 34

  34. Facebook & SNA: TouchGraph 35

  35. REFERENCES https://docs.google.com/viewer?a=v&pid=gmail&attid=0.1&thid=12cee7d02cc18b01&mt=application/pdf&url=https://mail.google.com/mail/?ui%3D2%26ik%3D06a2014ff8%26view%3Datt%26th%3D12cee7d02cc18b01%26attid%3D0.1%26disp%3Dattd%26realattid%3Df_ghol3ixg0%26zw&sig=AHIEtbT45SzIJqlr4ChSIpn8MLLaHo2fgw&pli=1 http://www.orgnet.com/sna.html 36

  36. http://www.davidkelly.ie/2008/09/19/twitter-social-network-analysis-apps/http://www.davidkelly.ie/2008/09/19/twitter-social-network-analysis-apps/ vlado.fmf.uni-lj.si/pub/networks/pajek/doc/pajekman.pdf http://www.davidkelly.ie/2008/09/19/twitter-social-network-analysis-apps/ 37

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