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    Finding Community Structures In Social Activity Data

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    Type
    Dissertation
    Authors
    Peng, Chengbin cc
    Advisors
    Keyes, David E. cc
    Committee members
    Moshkov, Mikhail cc
    Zhang, Xiangliang cc
    Ketcheson, David I. cc
    Wang, Suojin
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2015-05-19
    Permanent link to this record
    http://hdl.handle.net/10754/554139
    
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    Abstract
    Social activity data sets are increasing in number and volume. Finding community structure in such data is valuable in many applications. For example, understand- ing the community structure of social networks may reduce the spread of epidemics or boost advertising revenue; discovering partitions in tra c networks can help to optimize routing and to reduce congestion; finding a group of users with common interests can allow a system to recommend useful items. Among many aspects, qual- ity of inference and e ciency in finding community structures in such data sets are of paramount concern. In this thesis, we propose several approaches to improve com- munity detection in these aspects. The first approach utilizes the concept of K-cores to reduce the size of the problem. The K-core of a graph is the largest subgraph within which each node has at least K connections. We propose a framework that accelerates community detection. It first applies a traditional algorithm that is relatively slow to the K-core, and then uses a fast heuristic to infer community labels for the remaining nodes. The second approach is to scale the algorithm to multi-processor systems. We de- vise a scalable community detection algorithm for large networks based on stochastic block models. It is an alternating iterative algorithm using a maximum likelihood ap- proach. Compared with traditional inference algorithms for stochastic block models, our algorithm can scale to large networks and run on multi-processor systems. The time complexity is linear in the number of edges of the input network. The third approach is to improve the quality. We propose a framework for non- negative matrix factorization that allows the imposition of linear or approximately linear constraints on each factor. An example of the applications is to find community structures in bipartite networks, which is useful in recommender systems. Our algorithms are compared with the results in recent papers and their quality and e ciency are verified by experiments.
    Citation
    Peng, C. (2015). Finding Community Structures In Social Activity Data. KAUST Research Repository. https://doi.org/10.25781/KAUST-5CBCP
    DOI
    10.25781/KAUST-5CBCP
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-5CBCP
    Scopus Count
    Collections
    PhD Dissertations; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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