Link Prediction in Relational Data

Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define a joint probabilistic model over the entire link graph — entity attributes and links. The application of the RMN algorithm to this task requires the definition of probabilistic patterns over subgraph structures. We apply this method to two new relational datasets, one involving university webpages, and the other a social network. We show that

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Benjamin Taskar, Ming Fai Wong, Pieter Abbeel, Daphne Koller
• Keywords: Joint Probabilistic Model, Many Real-world Domains, NIPS 2003, NIPS 2007, Relational Markov Network