Social Networks Corina Ciubuc. Index Introduction Social Network Analysis (SNA) Metrics in SNA Example.

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