A Random Walk Tensor Model for Heterogeneous Network Entity Classification

Heterogeneous network entity classification is to predict the labels of entities in a heterogeneous network which consists of multiple types of relations and multiple types of entities. The existing studies have shown that relations information is critical for improving entity classification performance. The relation information is often exploited by clustering the entities into groups based on relation density or constructing relation features by counting the number of different relations. However, these methods only consider the relation information but neglect the importance of different relations in the network. In practice, different relations may have different degrees of importance w.r.t different entities. To this end, we propose a Random walk Tensor Model (RTM) to reveal the relation importance and classify the entities in the heterogeneous network, simultaneously. In RTM, the heterogeneous network is represented as a multi-relational network using a three-way tensor. The tensor is also used to compute the transition probability for the random walk among entities. We build a Markov chain model and use an iterative algorithm to solve the Markov chain equations in the model to obtain the random walk stationary distributions and compute the entity classification and relation ranking results based on such distributions. The theoretical analyses are given to show the rationality and interpretability of the model. The experimental results demonstrate that the RTM can achieve superior classification performance compared with several state-of-the-art methods and obtain a reasonable relation ranking.