Capturing social data evolution using graph clustering

IEEE Internet Computing

Volumn 17, Issue 1, 2013, Pages 74-79

  Giatsoglou, Maria ^a   Vakali, Athena ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Clustering; Data structures; Database applications; Database management; Graphs and networks; Information search and retrieval; Mining methods and algorithms; Pattern recognition; Web mining

Indexed keywords

CLUSTERING; DATABASE APPLICATIONS; DATABASE MANAGEMENT; GRAPHS AND NETWORKS; INFORMATION SEARCH AND RETRIEVAL; MINING METHODS AND ALGORITHMS; WEB MINING;

CLUSTERING ALGORITHMS; MINING; PATTERN RECOGNITION;

DATA STRUCTURES;

EID: 84873827896     PISSN: 10897801     EISSN: None     Source Type: Journal    
DOI: 10.1109/MIC.2012.141     Document Type: Article

References (15)

1. P. Mika, "Ontologies Are Us: A Unified Model of Social Networks and Semantics," Proc. Int'l Semantic Web Conf. (ISWC 05), Springer, 2005, pp. 522-536.
2. G. Palla et al., "Quantifying Social Group Evolution," Nature, vol. 446, no. 7136, 2007, pp. 664-667.
3. Y. Lin et al., "Blog Community Discovery and Evolution Based on Mutual Awareness Expansion," Proc. 2007 IEEE/WIC/ACM Int'l Conf. Web Intelligence (WI 07), IEEE CS, 2007, pp. 48-56.
4. J. Sun et al., "GraphScope: Parameter- Free Mining of Large Time-Evolving Graphs," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 07), ACM, 2007, pp. 687-696.
5. H. Tong et al., "Proximity Tracking on Time-Evolving Bipartite Graphs," Proc. SIAM Int'l Conf. Data Mining (SDM 08), SIAM, 2008, pp. 704-715.
6. Q. Zhao et al., "Temporal and Information Flow-Based Event Detection from Social Text Streams," Proc. 22nd Conf. Artificial Intelligence (AAAI 07), vol. 2, AAAI Press 2007, pp. 1501-1506.
7. D. Chakrabarti et al., "Evolutionary Clustering," Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 06), ACM, 2006, pp. 554-560.
8. Y. Chi et al., "Evolutionary Spectral Clustering by Incorporating Temporal Smoothness," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 07), ACM, 2007, pp. 153-162.
9. L. Tang et al., "Identifying Evolving Groups in Dynamic Multi-Mode Networks," IEEE Trans. Knowledge and Data Eng., vol. 24, no. 1, 2011, pp. 72-85.
10. Y. Lin et al., "MetaFac: Community Discovery via Relational Hypergraph Factorization," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 09), ACM, 2009, pp. 527-536.
11. D. Duan et al., "Community Mining on Dynamic Weighted Directed Graphs," Proc. Workshop Complex Networks in Information & Knowledge Management (CNIKM 09), ACM, 2009, pp. 11-18.
12. J. Sun et al., "Incremental Tensor Analysis: Theor y and Appl icat ions," ACM Trans. Knowledge Discovery from Data (TKDD), vol. 2, no. 3, 2008, article no. 11, pp. 1-37.
13. H. Ning et a l., "Incremental Spect ral Clustering by Efficiently Updating the Eigen-System," Pattern Recognition, vol. 43, no. 1, pp. 113-127.
14. D. Duan et al., "Incremental K-Clique Clustering in Dynamic Social Networks," Artificial Intelligence Rev., vol. 38, no. 2, 2011, pp. 129-147.
15. C. Bockermann et al., "Stream-Based Community Discovery via Relational Hypergraph Factorization on Evolving Networks," Proc. Workshop on Dynamic Networks and Knowledge Discovery (DyNaK 10), CEUR Workshop Proc., 2010, pp. 41-52.