Network mining and analysis for social applications

Publication Type

Conference Proceeding Article

Publication Date

8-2014

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.

Discipline

Digital Communications and Networking | Numerical Analysis and Scientific Computing

Publication

KDD'14 : proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 24-27, 2014, New York, NY, USA

First Page

1974

Last Page

1974

ISBN

978-1-4503-2956-9

Identifier

10.1145/2623330.2630810

Publisher

ACM New York, NY, USA ©2014

City or Country

New York, USA

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