A Survey on LDA Approach in Predicting Link Behavior in Social Networks
- Abdul Naveed Mastan, Mohammad Abdul Naveed Mastan, Ravi kishan
An ontology can be seen as an explicit description of the concepts and relationships that exist in a domain. In this paper, we address the problem of building an interest ontology and predicting potential friendship relations between users in the social network Live Journal, using features constructed based on the interest ontology. Previous work has shown that the accuracy of predicting friendship links in this network is very low if simply interests common to two users are used as features and no network graph features are considered. Thus, our goal is to organize users’ interests in an ontology (specifically, a concept hierarchy) and to use the semantics captured by this ontology to improve the performance of learning algorithms at predicting if two users can be friends. We have designed and implemented a hybrid clustering algorithm, which combines hierarchical agglomerative and divisive clustering paradigms, and automatically builds the interest ontology. Furthermore, we have explored the use of this ontology to construct interest-based features and shown that the resulting features improve the performance of various classifiers for predicting friendship links.