%0 Conference Paper %1 Zhou06interactionsAuthors %A Zhou, Ding %A Ji, Xiang %A Zha, Hongyuan %A Giles, C. Lee %B CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management %C New York, NY, USA %D 2006 %I ACM %K Zhou06interactionsAuthors folksonomy modelling toread %P 248--257 %R http://doi.acm.org/10.1145/1183614.1183653 %T Topic evolution and social interactions: how authors effect research %U http://portal.acm.org/citation.cfm?id=1183653&dl=GUIDE&coll=GUIDE&CFID=64167610&CFTOKEN=59359426 %X 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. %@ 1-59593-433-2

@inproceedings{Zhou06interactionsAuthors, 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.}, added-at = {2008-11-12T20:29:35.000+0100}, address = {New York, NY, USA}, author = {Zhou, Ding and Ji, Xiang and Zha, Hongyuan and Giles, C. Lee}, biburl = {https://www.bibsonomy.org/bibtex/2fe3862735b8f61dcafd9b8e6652f1dac/lee_peck}, booktitle = {CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management}, description = {Topic evolution and social interactions}, doi = {http://doi.acm.org/10.1145/1183614.1183653}, interhash = {35c55ed9e1c2068cc498e1975dd9ce32}, intrahash = {fe3862735b8f61dcafd9b8e6652f1dac}, isbn = {1-59593-433-2}, keywords = {Zhou06interactionsAuthors folksonomy modelling toread}, location = {Arlington, Virginia, USA}, pages = {248--257}, publisher = {ACM}, timestamp = {2008-11-12T20:29:35.000+0100}, title = {Topic evolution and social interactions: how authors effect research}, url = {http://portal.acm.org/citation.cfm?id=1183653&dl=GUIDE&coll=GUIDE&CFID=64167610&CFTOKEN=59359426}, year = 2006 }