Integration of scientific and social networks
In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks (i.e. The DBLP publication network and the Twitter social network). This task is a crucial step toward building a multi environment expert finding system that has recently attracted much attention in Information Retrieval community. In this paper, the problem of social and scientific network integration is divided into two sub problems. The first problem concerns finding those profiles in one network, which presumably have a corresponding profile in the other network and the second problem concerns the name disambiguation to find true matching profiles among some candidate profiles for matching. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. Because the labels of these candidate pairs are not independent, state-of-the-art classification methods such as logistic regression and decision tree, which classify each instance separately, are unsuitable for this task. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. Two main types of dependencies among candidate pairs are considered for designing the joint label prediction model which are quite intuitive and general. Using the discriminative approaches, we utilize various feature sets to train our proposed classifiers. An extensive set of experiments have been conducted on six test collection collected from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.