“With a little help from new friends”: Boosting information cascades in social networks based on link injection |
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Affiliation: | 1. Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;2. University of Eichstätt-Ingolstadt, Germany;1. Division of Cardiovascular Surgery, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada;2. Division of Cardiothoracic Surgery, Department of Surgery, University of Utah, Salt Lake City, Utah;3. Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas;1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China;2. Research Institute of Information Technology, Tsinghua University, Beijing 100084, China;3. Tsinghua National Lab for Information Science and Technology, Tsinghua University, Beijing 100084, China |
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Abstract: | We investigate information cascades in the context of viral marketing applications. Recent research has identified that communities in social networks may hinder cascades. To overcome this problem, we propose a novel method for injecting social links in a social network, aiming at boosting the spread of information cascades. Unlike the proposed approach, existing link prediction methods do not consider the optimization of information cascades as an explicit objective. In our proposed method, the injected links are being predicted in a collaborative-filtering fashion, based on factorizing the adjacency matrix that represents the structure of the social network. Our method controls the number of injected links to avoid an “aggressive” injection scheme that may compromise the experience of users. We evaluate the performance of the proposed method by examining real data sets from social networks and several additional factors. Our results indicate that the proposed scheme can boost information cascades in social networks and can operate as a “people recommendations” strategy complementary to currently applied methods that are based on the number of common neighbors (e.g., “friend of friend”) or on the similarity of user profiles. |
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Keywords: | Information cascades Viral marketing Social networks Matrix factorization |
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