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this assignment is to implement graph-based data analysis functions that could be used to identify influencers

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Assignment 2

Social Network Analysis

Aims

To implement graph-based data analysis functions to mine a given social network

To give you further practice with C and data structures (Graph ADT)

Introduction

The main focus of this assignment is to implement graph-based data analysis functions that could be used to identify influencers, followers and communities in a given social network.

Setting Up

Change into the directory you created for the assignment and run the following command:

unzip /web/cs2521/22T1/ass/ass2/downloads/files.zip

If you're working at home, download files.zip by clicking on the above link and then unzip the downloaded file.

This bundle of files includes the files you need to implement, as well as a few completed ADTs that you may use, some testing programs/scripts and sample graphs.

We have provided complete implementations of the Graph and Priority Queue ADTs. You may use these ADTs for any parts of the assignment as long as you do not modify them.

Part 1 - Dijkstra's Algorithm

In order to discover influencers, we need to repeatedly find shortest paths between pairs of nodes. Your task is to implement a variant Dijkstra's algorithm to discover the shortest paths from a given source node to all other nodes in the graph. The algorithm has one important additional feature: if there are multiple shortest paths from a source node to another node, it keeps track of all of them by allowing each node to have multiple predecessors. In the code, this is achieved by each node having a linked list of predecessors (see Dijkstra.h). Each predecessor list must be in ascending order.

In the following example, while discovering shortest paths from node 0, we find that there are two possible shortest paths from node 0 to node 1 (0 -> 1 and 0 -> 2 -> 1), so node 1 has two possible predecessors: node 0 and node 2, as shown below.

 

Source node: 0

  Distances

    0: 0

    1: 2

    2: 1

  Predecessors

    0: NULL

    1: [0] -> [2] -> NULL

    2: [0] -> NULL

 

Source node: 1

  Distances

    0: 2

    1: 0

    2: 3

  Predecessors

    0: [1] -> NULL

    1: NULL

    2: [0] -> NULL

 

Source node: 2

  Distances

    0: 3

    1: 1

    2: 0

  Predecessors

    0: [1] -> NULL

    1: [2] -> NULL

    2: NULL

 

What you need to do:

Complete the file Dijkstra.c that implements all the functions declared in Dijkstra.h.

Part 2 - Centrality Measures for Social Network Analysis

Centrality measures play a very important role in analysing a social network. For example, nodes with higher betweenness often correspond to influencers in a social network. Your task is to implement two well-known centrality measures for a given directed weighted graph.

Closeness Centrality

The closeness centrality of a node x is calculated as the reciprocal of the sum of the lengths of the shortest paths between node x and all other nodes y (y≠x) in the graph. Generally closeness is defined as below:

C(x)=1∑yd(y,x)

where d(y,x) is the shortest distance between vertices x and y.

 

 

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