Expertise Search in a Time-Varying Social Network

This paper is concerned with the problem of expertise search in a time-varying social network. Previous research work on expertise search, aiming at finding the most important/authoritative objects, usually ignores an important factor - temporal information, which reveals a huge amount of information contained in large document collections. Many real-world applications, for example reviewers matching for academic papers and hot-topic finding from newsgroup posts need to consider the evolution of information over times. In this work, we propose a unified model by integrating the temporal information into a random walk model. Specifically, the time information is modelled in a forward-and-backward propagation process in the random walk. The proposed model has been applied to expertise search in an academic social network. Experimental results show that the proposed approach can significantly outperform the baseline methods of using the language model (2.0% in terms of MAP) and the traditional PageRank algorithm (17.2% in terms of MAP).