International Journal of Man-Machine Studies
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Qualitative simulation of construction performance using fuzzy cognitive maps
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Statistical analysis of the social network and discussion threads in slashdot
Proceedings of the 17th international conference on World Wide Web
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A New Approach to Detect Communities in Multi-weighted Co-authorship Networks
SCCC '10 Proceedings of the 2010 XXIX International Conference of the Chilean Computer Science Society
How to become a group leader? or modeling author types based on graph mining
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Visualizing Bibliographic Databases as Graphs and Mining Potential Research Synergies
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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Co-authorship networks contain a wealth of information from the collaboration of the researchers, the research areas of individual authors to the detection of research communities and much more. Its applications in the field of learning and research include expert detection in a research area to the identification of institutions that involve in research on any particular area. With the exponential rise in research in the recent past and the enormity of the Co-authorship networks, research on mining knowledge from them has been an active area of research. We propose an enhancement to the co-authorship network. New edges are introduced based on the similarity within abstracts. This model would help in identification of potential research synergies even when the authors don't share a common edge in the original co-authorship graph. The initial experiments show that the modifications in the graph improve the co-authorship network in a semantic perspective.