Ranking in heterogeneous networks with geo-location information

Abstract

Entity ranking by importance or authority through relational information is an important problem in network science. A large body of existing work addresses the problem for homogeneous networks. With the emergence of richer networks, containing various types of entities and meta-data (e.g., attributes) in which edges carry rich semantic information, it becomes essential to build models that can leverage all available data in a meaningful way. In this work, we consider the ranking problem in heterogeneous information networks (HIN) with side information. Specifically, we introduce a new model called HINSIDE that has two key properties: (i) it explicitly represents the interactions (i.e., authority transfer rates or ATR) between different types of nodes, and (ii) it carefully incorporates the geo-location information of the entities to account for the distance and the competition between them. Besides an intuitive local formula, our model has a matrix form for which we derive a closed-form solution. Thanks to its closed form, HINSIDE lends itself to be used within various learning-to-rank objectives, for the estimation of its parameters (the ATR) provided training data. We formulate two kinds of objective functions for parameter learning with efficient estimation procedures. We validate the effectiveness of our proposed model and the learning procedures on samples from two real-world graphs, where we show the advantages of HINSIDE over popular existing models, including Pagerank and degree centrality.