HySoN: A Distributed Agent-Based Protocol for Group Formation in Online Social Networks

On-line social networks allow people to easily interact with each other by means of social computer services. This scenario makes possible to search in a social network for affinities or new opportunities that satisfy specific requirements. However, for many users such activities often imply undesirable accesses to personal sensitive data. In this scenario we propose a novel approach, called HySoN (Hyperspace Social Network), based on an overlay network of software agents. HySoN allows users to locally maintain sensitive user’s data, satisfying the privacy requirements preserving sensitive data. Indeed, the properties involved in the HySoN user aggregation are inferred by local data not published in the social network. Some experimental results obtained on simulated on-line social networks data show the searching of suitable nodes is very efficient due to the topology of the overlay network, which exhibits the small-world properties.

[1]  K. Marx Selected writings in sociology and social philosophy , 1956 .

[2]  M. V. Valkenburg Network Analysis , 1964 .

[3]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[4]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[5]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[6]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[7]  T. Postmes,et al.  The Formation of Group Norms in Computer-Mediated Communication , 2000 .

[8]  Manuel Castells,et al.  The Internet Galaxy: Reflections on the Internet, Business, and Society , 2001 .

[9]  Matthias Klusch,et al.  Information agent technology for the Internet: A survey , 2001, Data Knowl. Eng..

[10]  Ian T. Foster,et al.  A peer-to-peer approach to resource location in grid environments , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[11]  Ian Foster,et al.  A peer-to-peer approach to resource location in grid environments , 2002 .

[12]  Domenico Talia,et al.  Toward a Synergy Between P2P and Grids , 2003, IEEE Internet Comput..

[13]  Santosh S. Vempala,et al.  On clusterings: Good, bad and spectral , 2004, JACM.

[14]  Bart Selman,et al.  Tracking evolving communities in large linked networks , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Giuseppe M. L. Sarnè,et al.  Modeling cooperation in multi-agent communities , 2004, Cognitive Systems Research.

[16]  A. Moore,et al.  Dynamic social network analysis using latent space models , 2005, SKDD.

[17]  Gabrielle Demange,et al.  A Survey of Network Formation Models: Stability and Efficiency , 2005 .

[18]  S. Wasserman,et al.  Models and Methods in Social Network Analysis , 2005 .

[19]  Marie desJardins,et al.  Agent-organized networks for dynamic team formation , 2005, AAMAS '05.

[20]  Ben Shneiderman,et al.  Balancing Systematic and Flexible Exploration of Social Networks , 2006, IEEE Transactions on Visualization and Computer Graphics.

[21]  M. Newman,et al.  Nonequilibrium phase transition in the coevolution of networks and opinions. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[23]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[24]  Wan-Shiou Yang,et al.  Mining Social Networks for Targeted Advertising , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[25]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[26]  António Lucas Soares,et al.  Improving the quality of collaboration requirements for information management through social networks analysis , 2007, Int. J. Inf. Manag..

[27]  Julie A. McCann,et al.  A survey of autonomic computing—degrees, models, and applications , 2008, CSUR.

[28]  Bill Howard,et al.  Analyzing online social networks , 2008, Commun. ACM.

[29]  Ulrik Brandes,et al.  Engineering graph clustering: Models and experimental evaluation , 2008, JEAL.

[30]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[31]  Guillaume Pierre,et al.  Autonomous Resource Selection for Decentralized Utility Computing , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[32]  Theodoros Lappas,et al.  Finding a team of experts in social networks , 2009, KDD.

[33]  Borko Furht,et al.  Handbook of Cloud Computing , 2010 .

[34]  Luca Becchetti,et al.  Power in unity: forming teams in large-scale community systems , 2010, CIKM.

[35]  Corrado Santoro,et al.  HYGRA: A decentralized protocol for resource discovery and job allocation in large computational Grids , 2010, The IEEE symposium on Computers and Communications.

[36]  Cheng-Te Li,et al.  Team Formation for Generalized Tasks in Expertise Social Networks , 2010, 2010 IEEE Second International Conference on Social Computing.

[37]  Nitesh V. Chawla,et al.  Identifying and evaluating community structure in complex networks , 2010, Pattern Recognit. Lett..

[38]  Corrado Santoro,et al.  Exploiting the Small-World Effect for Resource Finding in P2P Grids/Clouds , 2011, 2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[39]  Andrei A. Bulatov,et al.  Complexity of conservative constraint satisfaction problems , 2011, TOCL.

[40]  Corrado Santoro,et al.  ComplexSim: An SMP-Aware Complex Network Simulation Framework , 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems.

[41]  Luca Becchetti,et al.  Online team formation in social networks , 2012, WWW.

[42]  Boleslaw K. Szymanski,et al.  Team Formation in Social Networks , 2012, ISCIS.

[43]  Giuseppe M. L. Sarnè,et al.  A Trust-Based Approach for a Competitive Cloud/Grid Computing Scenario , 2012, IDC.

[44]  Atish Das Sarma,et al.  Multi-skill Collaborative Teams based on Densest Subgraphs , 2011, SDM.

[45]  Corrado Santoro,et al.  Decentralised Resource Finding in Cloud/Grid Computing Environments: A Performance Evaluation , 2012, 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[46]  Giuseppe M. L. Sarnè,et al.  Integrating trust measures in multiagent systems , 2012, Int. J. Intell. Syst..

[47]  Corrado Santoro,et al.  ComplexSim: a flexible simulation platform for complex systems , 2013, Int. J. Simul. Process. Model..

[48]  Giancarlo Fortino,et al.  Intelligent Distributed Computing VI - Proceedings of the 6th International Symposium on Intelligent Distributed Computing - IDC 2012, Calabria, Italy, September 2012 , 2013, IDC.

[49]  Giuseppe M. L. Sarnè,et al.  Matching Users with Groups in Social Networks , 2013, IDC.

[50]  Costin Badica,et al.  Intelligent Distributed Computing VII - Proceedings of the 7th International Symposium on Intelligent Distributed Computing, IDC 2013, Prague, Czech Republic, September 2013 , 2014, IDC.

[51]  Giuseppe Pappalardo,et al.  Providing QoS strategies and cloud‐integration to web servers by means of aspects , 2015, Concurr. Comput. Pract. Exp..