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Network mining and analysis for social applications

Published: 24 August 2014 Publication History

Abstract

The recent blossom of social network and communication services in both public and corporate settings have generated a staggering amount of network data of all kinds. Unlike the bio-networks and the chemical compound graph data often used in traditional network mining and analysis, the new network data grown out of the social applications are characterized by their rich attributes, high heterogeneity, enormous sizes and complex patterns of various semantic meanings, all of which have posed significant research challenges to the graph/network mining community. In this tutorial, we aim to examine some recent advances in network mining and analysis for social applications, covering a diverse collection of methodologies and applications from the perspectives of event, relationship, collaboration, and network pattern. We would present the problem settings, the challenges, the recent research advances and some future directions for each perspective. Topics include but are not limited to correlation mining, iceberg finding, anomaly detection, relationship discovery, information flow, task routing, and pattern mining.

Supplementary Material

Part 1 of 3 (p1974-sidebyside1.mp4)
Part 2 of 3 (p1974-sidebyside2.mp4)
Part 3 of 3 (p1974-sidebyside3.mp4)

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  • (2014)A Paralleled Big Data Algorithm with MapReduce Framework for Mining Twitter DataProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.26(121-128)Online publication date: 3-Dec-2014

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  1. Network mining and analysis for social applications

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    cover image ACM Conferences
    KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2014
    2028 pages
    ISBN:9781450329569
    DOI:10.1145/2623330
    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 bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 24 August 2014

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    Author Tags

    1. network analysis
    2. network mining
    3. social applications

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    KDD '14
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    Acceptance Rates

    KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2014)A Paralleled Big Data Algorithm with MapReduce Framework for Mining Twitter DataProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.26(121-128)Online publication date: 3-Dec-2014

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