Investigating the Effect of Multiple Communities on Kernel-Based Citation Analysis

Abstract

In this paper, we discuss issues raised by applying Kandola et al.'s Neumann kernels to large citation graphs that have multiple communities. Neumann kernels can identify not only documents related a given document but also the most important documents in a citation graph. However, when Neumann kernels are biased towards importance, topranked documents are uniformly documents in the dominant community of the citation graph irrespective of the communities where the target document is cited. To solve this problem, we model a generation process of citations by probabilistic Latent Semantic Indexing, and then construct a weighted graph (hidden topic graph) for each community (topic). Applying Neumann kernels to each hidden topic graph, we can rank documents on the basis of the communities in which they appear.