In\ufb02uence maximization is the problem of \ufb01nding a small subset of nodes (seed nodes) in a social network that could maximize the spread of in\ufb02uence. In this paper, we study the ef\ufb01cient in\ufb02uence maximization from two complementary directions. One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves in\ufb02uence spread. We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org. Our experimental results show that (a) our improved greedy algorithm achieves better running time comparing with the improvement of [7] with matching in\ufb02uence spread,(b) our degree discount heuristics achieve much better in\ufb02uence spread than classic degree and centrality-based heuristics, and when tuned for a speci\ufb01c in\ufb02uence cascade model, it achieves almost matching in\ufb02uence thread with the greedy algorithm, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes. Based on our results, we believe that \ufb01ne-tuned heuristics may provide truly scalable solutions to the in\ufb02uence maximization problem with satisfying in\ufb02uence spread and blazingly fast running time. Therefore, contrary to what implied by the conclusion of [5] that traditional heuristics are outperformed by the greedy approximation algorithm, our results shed new lights on the research of heuristic algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
In\ufb02uence maximization is the problem of \ufb01nding a small subset of nodes (seed nodes) in a social network that could maximize the spread of in\ufb02uence. In this paper, we study the ef\ufb01cient in\ufb02uence maximization from two complementary directions. One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13559],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-246722","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-social-sciences","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2009), 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