Neural Networks for Fast Estimation of Social Network Centrality Measures

TLDR: It is shown that neural networks can be effective in learning and estimating the ordering of vertices in a social network based on centrality measures, requiring far less computational effort, and proving to be faster than early termination of the power grid method that can be used for computing these measures.

Abstract: Centrality measures are extremely important in the analysis of social networks, with applications such as identification of the most influential individuals for effective target marketing. Eigenvector centrality and PageRank are among the most useful centrality measures, but computing these measures can be prohibitively expensive for large social networks. This paper shows that neural networks can be effective in learning and estimating the ordering of vertices in a social network based on these measures, requiring far less computational effort, and proving to be faster than early termination of the power grid method that can be used for computing the centrality measures. Two features describing the size of the social network and two vertex-specific attributes sufficed as inputs to the neural networks, requiring very few hidden neurons.