The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Uncoverning Groups via Heterogeneous Interaction Analysis
Abstract
With the pervasive availability of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment shared content (bookmark, photos, videos), and users can tag their own favorite content. Users can also connect to each other, and subscribe to or become a fan or a follower of others. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members sharing certain similarities. It is challenging to effectively integrate the network information of multiple dimensions in order to discover cross-dimension group structures. In this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. We extract structural features from each dimension of the network via modularity analysis, and then integrate them all to find out a robust community structure among actors. Experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale.
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Cites
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Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
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Relational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799
Self-citationScalable learning of collective behavior based on sparse social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646094 · Cited by 233
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Cited by
Representation Learning for Attributed Multiplex Heterogeneous Network
Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., & Tang, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330964 · Cited by 465
A classification for community discovery methods in complex networks
Coscia, M., Giannotti, F., & Pedreschi, D. (2011) · Statistical Analysis and Data Mining: The ASA Data Science Journal · doi:10.1002/sam.10133 · Cited by 353
Business Intelligence in BLOGS: Understanding Consumer Interactions and Communities1
Chau, M., & Xu, J. (2012) · MIS Quarterly · doi:10.2307/41703504 · Cited by 347
Metadata
- Authors
- Tang, L., Wang, X., & Liu, H.
- Venue
- 2009 Ninth IEEE International Conference on Data Mining
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 503-512
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2095711515
- Cited by
- 162
- References
- 6
- FWCI
- 10.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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