restarac / close2center

Social Network - Closeness Centrality

How to run

Every subproject has its own README.md file that describes your uses, configurations, examples, notes...

• CORE (the implementation of the algorithm)

Briefing

In this challenge, suppose we are looking to do social network analysis for prospective customers. We want to extract from their social network a metric called "closeness centrality".

Centrality metrics try to approximate a measure of influence of an individual within a social network. The distance between any two vertices is their shortest path. The farness of a given vertex v is the sum of all distances from each vertex to v. Finally, the closeness of a vertex v is the inverse of the farness.

Part One

The first part of the challenge is to rank the vertices in a given undirected graph by their closeness. The graph is provided in the attached file; each line of the file consists of two vertex names separated by a single space, representing an edge between those two nodes.

Part Two

The second part of the challenge is to create a RESTful web server with endpoints to register edges and to render a ranking of vertexes sorted by centrality. We can think of the centrality value for a node as an initial "score" for that customer.

Part Three

The third and final part is to add another endpoint to flag a customer node as "fraudulent". It should take a vertex id, and update the internal customer score as such:

• The fraudulent customer score should be zero.
• Customers directly related to the "fraudulent" customer should have their score halved.
• More generally, scores of customers indirectly referred by the "fraudulent" customer should be multiplied by a coefficient F:

F(k) = (1 - (1/2)^k)

where k is the length of the shortest path from the "fraudulent" customer to the customer in question.

References:

• Closeness Centrality: http://en.wikipedia.org/wiki/Centrality#Closeness_...
• Shortest path: [http://en.wikipedia.org/wiki/Shortest_path_problem]