When a \u201cfollowing\u201d link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of \u201cfollowing\u201d links by proposing a model to describe this link diffusion process. To estimate the diffusion strength between different links, we \ufb01rst conduct an analysis on the diffusion effect in 24 triadic structures and \ufb01nd evident patterns that facilitate the effect. We then learn the diffusion strength in different triadic structures by maximizing an objective function based on the proposed model. The learned diffusion strength is evaluated through the task of link prediction and utilized to improve the applications of follower maximization and followee recommendation, which are speci\ufb01c instances of in\ufb02uence maximization. Our experimental results reveal that incorporating diffusion patterns can indeed lead to statistically signi\ufb01cant improvements over the performance of several alternative methods, which demonstrates the effect of the discovered patterns and diffusion model.<\/p>\n","protected":false},"excerpt":{"rendered":"
When a \u201cfollowing\u201d link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of \u201cfollowing\u201d links by proposing a model to describe this link diffusion process. To estimate the diffusion strength between different links, we \ufb01rst conduct an analysis on […]<\/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":[13561,13563,13559],"msr-publication-type":[193715],"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-246368","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-data-platform-analytics","msr-research-area-social-sciences","msr-locale-en_us"],"msr_publishername":"","msr_edition":"IEEE Transactions on Knowledge and Data Engineering, 37(8), 2015, 2093 - 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