social networks corina ciubuc. index introduction social network analysis (sna) metrics in sna...
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Social NetworksCorina Ciubuc
Index
• Introduction
• Social Network Analysis (SNA)
• Metrics in SNA
• Example
Social Networks
What is a Social Network ?
• Network – a set of nodes, points or locations connected
• Social Network - a social structure made up of individuals (or organizations) called "nodes", which are tied (connected) by one or more specific types of interdependency, such as friendship, common interest
• Social Network Analysis (SNA) - views social relationships in terms of network theory consisting of nodes and ties (also called edges, links or connections).
Social Network Analysis
• Nodes - individual actors within the networks • Ties - relationships between the actors • The resulting graph-based structures are often very complex• To understand networks and their participants, we evaluate the location of actors in the
network.
Measures in SNA
• Degree - The count of the number of ties to
other actors in the network. CD(v) = deg(v)
• Betweenness - The extent to which a node lies between other nodes in the network. The measure reflects the number of people who a person is connecting indirectly through their direct links.
Measures in SNA (2)
• Closeness - The degree an individual is near all other individuals in a network (directly or indirectly). Closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network.
Measures in SNA (3)
• Bridge - An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. Tarjan Algorithm
• Centralization - The difference between the number of links for each node divided by maximum possible sum of differences.
Measures in SNA (4)
• Eigenvector centrality - A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.
• Local bridge - An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle.
• Path length - The distances between pairs of nodes in the network.
Example: Kite Network
Example (2)
• Degree Centrality - Diane has the most direct connections in the network - the most active node in the network = a 'connector' or 'hub' in the network.
“The more connections, the better."
…… This is not always true
• Betweenness Centrality - Heather has few direct connections …. but she has one of the best locations in the network = a 'broker' in the network.
“ Location, Location, Location."
Example (3)
• Closeness Centrality - Fernando and Garth The pattern of their direct and indirect ties allow them to access all the nodes in the network more quickly than anyone else = they have the best visibility into what is happening in the network.
• Boundary Spanners - Nodes that connect their group to others usually end up with high network metrics.
• Network Centralization - A very centralized network is dominated by one or a few very central nodes.
References
• “Social Network” – Retrieved from - http://en.wikipedia.org/wiki/Social_network
• “Social Network Analysis, A Brief Introduction” Retrieved from -http://www.orgnet.com/sna.html
• Mislov A., Marcon M., Gummadi K., Druschel P., Bhattacharjee B - “Measurement and Analysis of Online Social Networks” - Retrieved from - http://conferences.sigcomm.org/imc/2007/papers/imc170.pdf
Questions