  Academic literature retrieval is concerned with the selection of papers that
are most likely to match a user's information needs. Most of the retrieval
systems are limited to list-output models, in which the retrieval results are
isolated from each other. In this work, we aim to uncover the relationships of
the retrieval results and propose a method for building structural retrieval
results for academic literatures, which we call a paper evolution graph (PEG).
A PEG describes the evolution of the diverse aspects of input queries through
several evolution chains of papers. By utilizing the author, citation and
content information, PEGs can uncover the various underlying relationships
among the papers and present the evolution of articles from multiple
viewpoints. Our system supports three types of input queries: keyword,
single-paper and two-paper queries. The construction of a PEG mainly consists
of three steps. First, the papers are soft-clustered into communities via
metagraph factorization during which the topic distribution of each paper is
obtained. Second, topically cohesive evolution chains are extracted from the
communities that are relevant to the query. Each chain focuses on one aspect of
the query. Finally, the extracted chains are combined to generate a PEG, which
fully covers all the topics of the query. The experimental results on a
real-world dataset demonstrate that the proposed method is able to construct
meaningful PEGs.
