Node importance estimation is a fundamental task in graph data analysis. Extensive studies have focused on this task, and various downstream applications have benefited from it, such as recommendation, resource allocation optimization, and missing value completion. However, existing works either focus on the homogeneous network or only study importance-based ranking. We are the first to consider the node importance values as heterogeneous values in heterogeneous information networks (HINs). A typical HIN is built of several distinguished node types where each type has its own measure of importance value (e.g., in the DBLP network, the importance values of authors and papers can be reflected by their h-index and citation numbers, respectively). This characteristic makes the above problem more challenging than computing the node importance in conventional homogeneous networks. In this paper, we formally introduce the problem of node importance value estimation in HINs; that is, given the importance values of a subset of nodes in an HIN, we aim to estimate the importance values of the remaining nodes. To solve this problem, we propose an effective graph neural network (GNN) model, called HIN Importance Value Estimation Network (HIVEN). HIVEN traces the local information of each node, specifically by utilizing the heterogeneity of the HIN. Furthermore, the meta schema is deployed to alleviate the node type domination issue. Additionally, HIVEN exploits the node similarity within each type to remedy the shortcoming of GNN models in capturing global information. Extensive experiments on real-world HIN datasets demonstrate that HIVEN superiorly outperforms the baseline methods.

Given a heterogeneous information network (HIN) H, a head node h , a meta-path P, and a tail node t , the meta-path prediction aims at predicting whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the A ttention mechanism and B i L STM for E mbedding. Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. A triplet embedding is then derived to complete the prediction. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively.