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Many real-world networks can be modeled by networks of interacting agents.Analysis of these interactions can reveal fundamental properties from thesenetworks. Estimating the amount of collaboration in a network corresponding toconnections in a learning environment can reveal to what extent learners sharetheir experience and knowledge with other learners. Alternatively, analyzingthe network of interactions in an open source software project can manifestindicators showing the efficiency of collaborations. One central problem insuch domains is the low cooperativity values of networks due to the lowcooperativity values of their respective communities. So administrators shouldnot only understand and predict the cooperativity of networks but also theyneed to evaluate their respective community structures. To approach this issue,in this paper, we address two domains of open source software projects andlearning forums. As such, we calculate the amount of cooperativity in thecorresponding networks and communities of these domains by applying severalcommunity detection algorithms. Moreover, we investigated the communityproperties and identified the significant properties for estimating the networkand community cooperativity. Correspondingly, we identified to what extentvarious community detection algorithms affect the identification of significantproperties and prediction of cooperativity. We also fabricated binary andregression prediction models using the community properties. Our results andconstructed models can be used to infer cooperativity of community structuresfrom their respective properties. When predicting high defective structures innetworks, administrators can look for useful drives to increase thecollaborations.

Investigating Cooperativity of Overlapping Community Structures in Social Networks