The problem of discovering information flow trends in social networks has become increasingly relevant due to the increasing amount of content in online social networks, and its relevance as a tool for research into the content trends analysis in the network. An important part of this analysis is to determine the key patterns of flow in the underlying network. Almost all the work in this area has focused on fixed models of the network structure, and edge-based transmission between nodes. In this article, we propose a fully content-centered model of flow analysis in networks, in which the analysis is based on actual content transmissions in the underlying social stream, rather than a static model of transmission on the edges. First, we introduce the problem of influence analysis in the context of information flow in networks. We then propose a novel algorithm InFlowMine to discover the information flow patterns in the network and demonstrate the effectiveness of the discovered information flows using an influence mining application. This application illustrates the flexibility and effectiveness of our information flow model to find topic- or network-specific influencers, or their combinations. We empirically show that our information flow mining approach is effective and efficient than the existing methods on a number of different measures.
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The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the DARPA, ARL, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. This research was sponsored by the Defense Advanced Research Project Agency (DARPA) agreement number W911NF-12-C-0028, Army Research Laboratory (ARL) cooperative agreement number W911NF-09-2-0053 and IBM Ph.D. fellowship. Authors’ addresses: K. Subbian, Computer Science Department, University of Minnesota, 200 Union St SE, Minneapolis, MN 55455; email: email@example.com; C. Aggarwal, IBM T.J. Watson Research Center, 1101 Route 134 Kitchawan Rd, Yorktown Heights, NY 10598; email: firstname.lastname@example.org; J. Srivastava, University of Minnesota, 200 Union St SE, Minneapolis, MN 55455; email: email@example.com. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or firstname.lastname@example.org. ©c 2016 ACM 1556-4681/2016/01-ART26 $15.00 DOI: http://dx.doi.org/10.1145/2815625
- Influencer analysis
- Information flows
- Network analysis