Integrating Data Mining Techniques and Social Network Model into Effective Framework and Its Application in Computational Biology

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Social network research is multidisciplinary requiring expertise from sociology, behavioral science, psychology, statistics, mathematics, computer science, etc. Recent development in information technology has highly influenced and shaped the research in social networks, however, the problem became challenging in that a more comprehensive and informative model could be constructed if the process could be extended for the huge amount of data characterizing the application. We argue that the model could be enriched by considering implicit links in addition to the explicit ones by employing data mining techniques. The resultant model will be used for more effective knowledge discovery. Defining the problem and building the model are the main issues facing the successful application of social network methodology in different domains. Data mining techniques are firstly used to realize the interactions between the actors in a social network. Once a social network model is constructed, data mining techniques can be used to further analyze the network in addition to existing metrics in social network analysis. This research will utilize various data mining techniques to derive the strength of the links in a social network; this will lead to stronger correlations between actors by considering them globally. Our major goals are: 1) Integrating data mining and social network model into a comprehensive and robust framework. 2) Realizing the effectiveness of the developed model by applying it to molecular interaction networks. 3) Adapting data mining techniques to facilitate the recognition of new molecular components. 4) Integrating large sets of heterogeneous data to build functional social networks. This research expands the power of existing software packages and advances towards a comprehensive product of social and scientific impact. There is a need for such tool in the social and scientific communities.