Topic evolution and social interactions: how authors effect research

ABSTRACT

We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003
• 3: P. Deuhard, W. Huisinga, A. Fischer, and C. Schutte. Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains. Linear Algebra and its Applications, 315(1--3):39--59, 2000.
• 4: Chris H. Q. Ding , Xiaofeng He , Hongyuan Zha , Ming Gu , Horst D. Simon, A Min-max Cut Algorithm for Graph Partitioning and Data Clustering, Proceedings of the 2001 IEEE International Conference on Data Mining, p.107-114, November 29-December 02, 2001
• 5: Gary William Flake , Steve Lawrence , C. Lee Giles, Efficient identification of Web communities, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.150-160, August 20-23, 2000, Boston, Massachusetts, United States [doi>10.1145/347090.347121]
• 6: C. Lee Giles , Kurt D. Bollacker , Steve Lawrence, CiteSeer: an automatic citation indexing system, Proceedings of the third ACM conference on Digital libraries, p.89-98, June 23-26, 1998, Pittsburgh, Pennsylvania, United States [doi>10.1145/276675.276685]
• 7: Daniel Gruhl , R. Guha , David Liben-Nowell , Andrew Tomkins, Information diffusion through blogspace, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988739]
• 8: Hui Han , Lee Giles , Hongyuan Zha , Cheng Li , Kostas Tsioutsiouliklis, Two supervised learning approaches for name disambiguation in author citations, Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, June 07-11, 2004, Tuscon, AZ, USA [doi>10.1145/996350.996419]
• 9: Thomas Hofmann, Unsupervised learning by probabilistic latent semantic analysis, Machine Learning, v.42 n.1/2, p.177-196, Jan. 2001 [doi>10.1023/A:1007617005950]
• 10: Henry Kautz , Bart Selman , Mehul Shah, Referral Web: combining social networks and collaborative filtering, Communications of the ACM, v.40 n.3, p.63-65, March 1997 [doi>10.1145/245108.245123]
• 11: Jeremy Kubica , Andrew Moore , Jeff Schneider , Yiming Yang, Stochastic link and group detection, Eighteenth national conference on Artificial intelligence, p.798-804, July 28-August 01, 2002, Edmonton, Alberta, Canada
• 12: David Liben-Nowell , Jon Kleinberg, The link prediction problem for social networks, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA [doi>10.1145/956863.956972]
• 13: A. J. Lotka. The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12):317--323, 1926.
• 14: Qiaozhu Mei , ChengXiang Zhai, Discovering evolutionary theme patterns from text: an exploration of temporal text mining, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081895]
• 15: Brad Miller , John Riedl, A hands-on introduction to collaborative filtering (tutorial session)(abstract only), Proceedings of the 1996 ACM conference on Computer supported cooperative work, p.5, November 16-20, 1996, Boston, Massachusetts, United States [doi>10.1145/240080.318367]
• 16: Satoshi Morinaga , Kenji Yamanishi, Tracking dynamics of topic trends using a finite mixture model, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1016919]
• 17: Kamal Nigam , Andrew Kachites McCallum , Sebastian Thrun , Tom Mitchell, Text Classification from Labeled and Unlabeled Documents using EM, Machine Learning, v.39 n.2-3, p.103-134, May-June 2000 [doi>10.1023/A:1007692713085]
• 18: E. Seneta. Non-negative Matrices and Markov Chains. Springer-Verlag, 1981.
• 19: T. Snijders. Models for longitudinal network data. Book Chapter in Models and methods in social network analysis, 2004.
• 20: Mark Steyvers , Padhraic Smyth , Michal Rosen-Zvi , Thomas Griffiths, Probabilistic author-topic models for information discovery, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014087]
• 21: S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge, New York, USA, 1994.
• 22: Yiming Yang , Jian Zhang , Jaime Carbonell , Chun Jin, Topic-conditioned novelty detection, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775150]
• 23: Ding Zhou , Eren Manavoglu , Jia Li , C. Lee Giles , Hongyuan Zha, Probabilistic models for discovering e-communities, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135807]

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.4 SOCIAL AND BEHAVIORAL SCIENCES

Additional Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
clustering, markov chains, social network analysis, text data mining