peppe-99 / CentralityMeasures-with-GPU

Sequential and parallel implementation of different Centrality Measures.

CentralityMeasures-with-GPU

This repository is dedicated to my master's degree project. The main goal is to implement, using GPU parallelism, algorithms that compute the "centrality" of each node and the "connectivity" of the network. For now, I have implemented these algorithms:

• Degree Centrality (sequential & parallel version): in this case the "centrality" of a node depends on its degree;
• Closeness Centrality (sequential & parallel version): here the "centrality" of a node depends on the distances separating it from other nodes in the network;
• Betweenness centrality (sequential & parallel version): here the "centrality" of a node depends on the number of "shortest paths" it partecipates in.

For the first two algorithms, the graph is represented by an adjacency matrix, a $n\times n$ matrix where an element in position $i,j$ is equal to $1$ if an edge exists from $i$ to $j$. Viceversa, for the third algoritm, the graph is represented with the Compressed Sparse Row (CSR) format. This representation consists of two arrays: column indices and row offsets. The column indices array is a concatenation of each vertex's adjacency list into an array of $m$ elements. Instead, the row offsets array is an $n+1$ element array that points at where each vertex's adjacency list begins and ends withis the column indices array.