document: Detecting research topics via the correlation between graphs and texts In this paper we address the problem of detecting topics in large-scale linked document collections . Recently , topic detection has become a very active area of research due to its utility for information navigation , trend analysis , and high-level description of data . We present a unique approach that uses the correlation between the distribution of a term that represents a topic and the link distribution in the citation graph where the nodes are limited to the documents containing the term . This tight coupling between term and graph analysis is distinguished from other approaches such as those that focus on language models . We develop a topic score measure for each term , using the likelihood ratio of binary hypotheses based on a probabilistic description of graph connectivity . Our approach is based on the intuition that if a term is relevant to a topic , the documents containing the term have denser connectivity than a random selection of documents . We extend our algorithm to detect a topic represented by a set of terms , using the intuition that if the co-occurrence of terms represents a new topic , the citation pattern should exhibit the synergistic effect . We test our algorithm on two electronic research literature collections , arXiv and Citeseer . Our evaluation shows that the approach is effective and reveals some novel aspects of topic detection .
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