Hot topic detection based on complex networks

The hot topic has become a key part of social information. Recognizing and detecting hot topics can help people to be aware of the focus of the community in the period and discover public opinions. The improved model can dynamically adjust the cluster to more accurately match document. Moreover, it lays the foundation for further study on the evolution of the hot topics in complex networks. For verifying the feasibility and validity of the model, the experiments are performed and the experimental results show that the proposed method works well on large-scale WebPage dataset.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[3]  Wen-Xu Wang,et al.  Detecting hidden nodes in complex networks from time series. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Yi-Ning Tu,et al.  Indices of novelty for emerging topic detection , 2012, Inf. Process. Manag..

[5]  Reynaldo Gil-García,et al.  A General Framework for Agglomerative Hierarchical Clustering Algorithms , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  Gary Geunbae Lee,et al.  Dependency structure language model for topic detection and tracking , 2007, Inf. Process. Manag..

[7]  Jin Zhang,et al.  An empirical study of sentiment analysis for chinese documents , 2008, Expert Syst. Appl..

[8]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[9]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[10]  Tim Leek,et al.  Probabilistic approaches to topic detection and tracking , 2002 .

[11]  Yang Jian-mei,et al.  The Complex Network Analysis of Competitive Relationships between Manufacturers in Foshan Ceramic Industry Cluster , 2006, 2006 International Conference on Management Science and Engineering.

[12]  Chalee Asavathiratham,et al.  The influence model: a tractable representation for the dynamics of networked Markov chains , 2001 .

[13]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[14]  Bruce J. West,et al.  Maximizing information exchange between complex networks , 2008 .

[15]  Xiao Han,et al.  Disease spreading with epidemic alert on small-world networks , 2007 .

[16]  Khyou Bun Khoo.  Topic trend detection and mining in world wide web , 2004 .

[17]  Javed I. Khan,et al.  Relationship Algebra for Computing in Social Networks and Social Network Based Applications , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[18]  James T. Kwok,et al.  Mining customer product ratings for personalized marketing , 2003, Decis. Support Syst..

[19]  Bruno Pouliquen,et al.  Multilingual and cross-lingual news topic tracking , 2004, COLING.

[20]  Ying Ding,et al.  Community detection: Topological vs. topical , 2011, J. Informetrics.

[21]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[22]  Satoshi Morinaga,et al.  Tracking dynamics of topic trends using a finite mixture model , 2004, KDD.

[23]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[24]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[25]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[26]  Jianping Zeng,et al.  Variable space hidden Markov model for topic detection and analysis , 2007, Knowl. Based Syst..

[27]  S. N. Dorogovtsev,et al.  Evolution of networks , 2001, cond-mat/0106144.

[28]  Masao Kubo,et al.  Lognormal Distribution of BBS Articles and its Social and Generative Mechanism , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[29]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[30]  J. Klafter,et al.  The random walk's guide to anomalous diffusion: a fractional dynamics approach , 2000 .

[31]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[32]  Per Ahlgren,et al.  Textual content, cited references, similarity order, and clustering : an experimental study in the context of science mapping , 2009 .