Perturb and Combine to Identify Influential Spreaders in Real-World Networks

Abstract

Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure. Inspired by bagging in Machine Learning, we propose the first Perturb and Combine (P&C) procedure for networks. It (1) creates many perturbed versions of a given graph, (2) applies a node scoring function separately to each graph, and (3) combines the results. Experiments conducted on real-world networks of various sizes with the k-core, generalized k-core, and PageRank algorithms reveal that P&C brings substantial improvements. Moreover, this performance boost can be obtained at almost no extra cost through parallelization. Finally, a bias-variance analysis suggests that P&C works mainly by reducing bias, and that therefore, it should be capable of improving the performance of all vertex scoring functions, including stable ones.

arXiv commentAccepted at ASONAM 2019

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• Efficient Estimation of Word Representations in Vector Space
• Identification of influential spreaders in complex networks
• The Dynamics of Viral Marketing
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• k-core (bootstrap) percolation on complex networks: Critical phenomena and nonlocal effects

Cited by in corpus (1)

• Identifying Key Nodes for the Influence Spread using a Machine Learning Approach