Semantic Divergence based Evaluation of Web Service Communities

Hafida Naim (1) , Mustapha Aznag (1) , Mohamed Quafafou (1) , Nicolas Durand (1)

1 LSIS - Laboratoire des Sciences de l'Information et des Systèmes

The number of community detection algorithms is growing continuously adopting a topological based approach to discover optimal subgraphs or communities. In this paper, we propose a new method combining both topology and semantic to evaluate and rank community detection algorithms. To achieve this goal we consider a probabilistic topic based approach to define a new measure called semantic divergence of communities. Combining this measure with others related to prior knowledge, we compute a score for each algorithm to evaluate the effectiveness of its communities and propose a ranking method. We have evaluated our approach considering communities of real web services.

Mots clés: Community detection algorithms; Web services; Service communities; Topic models; Semantic divergence; Dependency network

Domaines: Informatique [cs]

Hafida Naim, Mustapha Aznag, Mohamed Quafafou, Nicolas Durand. Semantic Divergence based Evaluation of Web Service Communities. IEEE 13th IEEE International Conference on Services Computing (SCC 2016), 2016, San Francisco, CA, United States. pp.736-743. ⟨hal-01465111⟩