Group and link analysis of multi-relational scientific social networks

Analyzing social networks enables us to detect several inter and intra connections between people in and outside their organizations. We model a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community. Finally, we evaluate the temporal evolution of scientific social networks to suggest/predict new relationships.

[1]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[2]  Ryutaro Ichise,et al.  Community mining tool using bibliography data , 2005, Ninth International Conference on Information Visualisation (IV'05).

[3]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Ramakrishnan Srikant,et al.  Mining newsgroups using networks arising from social behavior , 2003, WWW '03.

[5]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[6]  Jano Moreira de Souza,et al.  GCC: A Knowledge Management Environment for Research Centers and Universities , 2006, APWeb.

[7]  Srinivasan Parthasarathy,et al.  An event-based framework for characterizing the evolutionary behavior of interaction graphs , 2009, ACM Trans. Knowl. Discov. Data.

[8]  Michael Ley,et al.  DBLP - Some Lessons Learned , 2009, Proc. VLDB Endow..

[9]  Jon M. Kleinberg,et al.  Inferring Web communities from link topology , 1998, HYPERTEXT '98.

[10]  Hsinchun Chen,et al.  Link prediction approach to collaborative filtering , 2005, Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '05).

[11]  C. Lee Giles,et al.  Efficient identification of Web communities , 2000, KDD '00.

[12]  David Shallcross,et al.  Practical Issues and Algorithms for Analyzing Terrorist Networks 1 , 2002 .

[13]  Hong Yan,et al.  BSN: An automatic generation algorithm of social network data , 2011, J. Syst. Softw..

[14]  H. Milward,et al.  Dark Networks as Problems , 2003 .

[15]  Michael Negnevitsky,et al.  Artificial Intelligence Applications for Analysis of E-mail Communication Activities , 2004 .

[16]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[17]  Stephen Farrell,et al.  Relescope: an experiment in accelerating relationships , 2005, CHI Extended Abstracts.

[18]  Rory V. O'Connor,et al.  Development of a team measure for tacit knowledge in software development teams , 2009, J. Syst. Softw..

[19]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Hsinchun Chen,et al.  Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering , 2004, TOIS.

[21]  Jano Moreira de Souza,et al.  Mining and Analyzing Multirelational Social Networks , 2009, 2009 International Conference on Computational Science and Engineering.

[22]  Luis Otávio Alvares,et al.  An architecture based on multi-agent system and data mining for recommending research papers and researchers , 2006, SEKE.

[23]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

[24]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[25]  Jens Dietrich,et al.  Using social networking and semantic web technology in software engineering - Use cases, patterns, and a case study , 2008, J. Syst. Softw..

[26]  Jiawei Han,et al.  Community Mining from Multi-relational Networks , 2005, PKDD.

[27]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[28]  Jano Moreira de Souza,et al.  Mining and analyzing organizational social networks for collaborative design , 2009, 2009 13th International Conference on Computer Supported Cooperative Work in Design.

[29]  Richard M. Karp,et al.  Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems , 1972, Combinatorial Optimization.

[30]  Linyuan Lu,et al.  Role of weak ties in link prediction of complex networks , 2009, CIKM-CNIKM.

[31]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[32]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[33]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Luis Otávio Campos Alvares,et al.  A RECOMMENDER SYSTEM TO AID BRAZILIAN RESEARCHERS TO FIND INFORMATION AND CONTACT RELEVANT PEOPLE * , 2005 .

[35]  Isaac Olusegun Osunmakinde,et al.  Temporality in Link Prediction: Understanding Social Complexity , 2009 .

[36]  Michael F. Schwartz,et al.  Discovering shared interests using graph analysis , 1993, CACM.

[37]  D. Zanette.  Dynamics of rumor propagation on small-world networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Andrew Parker,et al.  The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations , 2004 .

[39]  Tamara G. Kolda,et al.  Link Prediction on Evolving Data Using Matrix and Tensor Factorizations , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[40]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, KDD 2012.

[41]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Bernardo A. Huberman,et al.  Email as spectroscopy: automated discovery of community structure within organizations , 2003 .

[43]  Michael Gertz,et al.  Mining email social networks , 2006, MSR '06.

[44]  Jens Dietrich,et al.  Using Social Networking and Semantic Web Technology in Software Engineering--Use Cases, Patterns, and a Case Study , 2007, 2007 Australian Software Engineering Conference (ASWEC'07).

[45]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[46]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[47]  Hai Zhuge,et al.  Virtual knowledge service market - For effective knowledge flow within knowledge grid , 2007, J. Syst. Softw..