Perturb and Combine to Identify Influential Spreaders in Real-World Networks
This addresses the problem of unreliable influential spreader identification in real-world networks for researchers and practitioners, offering a method to enhance existing algorithms with minimal extra cost, though it is incremental as it adapts an existing ML technique to networks.
The paper tackled the instability of influential spreader detection algorithms to small network perturbations by proposing a Perturb and Combine (P&C) procedure inspired by bagging, which creates perturbed graph versions, applies node scoring functions, and combines results, leading to substantial improvements in experiments on real-world networks with algorithms like k-core and PageRank.
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.