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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 138-145
Yibo Yao , School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
Lawrence B. Holder , School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
ABSTRACT
Traditional network classification techniques will become computationally intractable when applied on a network which is presented in a streaming fashion with continuous updates. In this paper, we examine the problem of classification in dynamic streaming networks, or graphs. Two scenarios have been considered: the graph transaction scenario and the one large graph scenario. We propose a unified framework consisting of three components: a subgraph extraction method, an online version of an existing graph kernel, and two kernel-based incremental learners. We demonstrate the advantages of our framework via empirical evaluations on several real-world network datasets.
INDEX TERMS
Kernel, Entropy, Social network services, Machine learning algorithms, Data mining, Support vector machines, Computational modeling
CITATION

Y. Yao and L. B. Holder, "Classification in dynamic streaming networks," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 138-145.
doi:10.1109/ASONAM.2016.7752225
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