On ranking relevant entities in heterogeneous networks using a language‐based model

Abstract

A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and networkbased scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSe-erX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.

A survey of heterogeneous information network analysis

“…For example, Zhou et al [119] co-rank authors and their publications by coupling two random walk processes, and co-HITS [120] incorporates the bipartite graph with the content information and the constraints of relevance. Soulier et al [121] propose a bi-type entity ranking algorithm to rank jointly documents and authors in a bibliographic network regarding a topical query by combining content-based and network-based features. There are also some ranking works on multi-relational network.…”

OLFinder: Finding opinion leaders in online social networks

“…Finding influential nodes in different kinds of networks ranging from heterogeneous scholarly networks [1–3] to protein interaction networks [4] is an important topic in the field of information science. Social networks are also a kind of network which is made up of a set of people and a set of relations between them.…”