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.
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Notes
- 1.
All the results reported in this paper were from simulations executed on an Intel Corei5@2.60 GHz machine with 8 GB RAM.
- 2.
Random network generation algorithms were obtained from [5, 16], and scale-free network generating algorithms were from [6, 15], using the implementation in the Python software package, NetworkX (http://networkx.github.io).
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Kumar, A., Mehrotra, K.G., Mohan, C.K. (2015). Neural Networks for Fast Estimation of Social Network Centrality Measures. In: Ravi, V., Panigrahi, B., Das, S., Suganthan, P. (eds) Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015). Advances in Intelligent Systems and Computing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-27212-2_14
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