A Survey on the Role of Centrality as Seed Nodes for Information Propagation in Large Scale Network

Abstract

From the popular concept of six-degree separation, social networks are generally analyzed in the perspective of small world networks where centrality of nodes play a pivotal role in information propagation. However, working with a large dataset of a scale-free network (which follows power law) may be different due to the nature of the social graph. Moreover, the derivation of centrality may be difficult due to the computational complexity of identifying centrality measures. This study provides a comprehensive and extensive review and comparison of seven centrality measures (clustering coefficients, Node degree, K-core, Betweenness, Closeness, Eigenvector, PageRank) using four information propagation methods (Breadth First Search, Random Walk, Susceptible-Infected-Removed, Forest Fire). Five benchmark similarity measures (Tanimoto, Hamming, Dice, Sorensen, Jaccard) have been used to measure the similarity between the seed nodes identified using the centrality measures with actual source seeds derived through Google's LargeStar-SmallStar algorithm on Twitter Stream Data. MapReduce has been utilized for identifying the seed nodes based on centrality measures and for information propagation simulation. It is observed that most of the centrality measures perform well compared to the actual source in the initial stage but are saturated after a certain level of influence maximization in terms of both affected nodes and propagation level.