Community discovery of public cloud web services based on structural networks

Community structure is the most common topological structure in complex networks. An important feature of complex networks is that they are generally composed of highly inner connected sub-networks called community. It is very useful and significant to understand the features of these communities. In this paper, we study the community structure of structural service networks formed by public web services available on the Internet. Firstly, we present a method for web service structural network construction based on web service description documents. Secondly, we propose a community discovery algorithm based on web service interaction relationships and analyze community structure of web service networks. Finally, we preform experiment on several real datasets, and the results show the efficiency and feasibility of community discovery algorithm.

[1]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[2]  A. Clauset.  Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Jörn Altmann,et al.  The structural evolution of the Web 2.0 service network , 2009, Online Inf. Rev..

[4]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[5]  Daniele Braga,et al.  Mashing Up Search Services , 2008, IEEE Internet Computing.

[6]  Joseph Christmas,et al.  The Decade and beyond , 1991 .

[7]  C. Peltz,et al.  Web Services Orchestration and Choreography , 2003, Computer.

[8]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[9]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Dongwon Lee,et al.  Graph Theoretic Topological Analysis of Web Service Networks , 2009, World Wide Web.

[11]  Alessandro Vespignani.  Modelling dynamical processes in complex socio-technical systems , 2011, Nature Physics.

[12]  Ma Yu.  Empirical Study on the Characteristics of Complex Networks in Networked Software , 2011 .

[13]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[14]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[15]  Bing Li,et al.  Empirical Study on the Characteristics of Complex Networks in Networked Software: Empirical Study on the Characteristics of Complex Networks in Networked Software , 2011 .

[16]  M. A. Muñoz,et al.  Journal of Statistical Mechanics: An IOP and SISSA journal Theory and Experiment Detecting network communities: a new systematic and efficient algorithm , 2004 .

[17]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[18]  Amit P. Sheth,et al.  Services Mashups: The New Generation of Web Applications , 2008, IEEE Internet Computing.

[19]  Stephen S. Yau,et al.  Toward Development of Adaptive Service-Based Software Systems , 2009, IEEE Transactions on Services Computing.