Node ranking in heterogeneous graphs, which quantifies the relative importance of nodes, can often be improved by incorporating information from relevant paths. Graph database and heterogeneous graph neural network (HGNN) are two main approaches to better solve this problem. Graph databases support efficient path queries for flexible path types but require manual design to combine results for node ranking. Conversely, current HGNNs can automatically integrate semantic information from multiple linear path types for accurate node ranking. However, our experiments show that they fail to outperform a multi-layer perceptron model that utilizes features extracted from multiple nonlinear conditional paths, which can be handled by graph databases. Therefore, we aim to enable HGNN to take advantage of these path types for better performance. However, HGNNs require a generalized path schema to define the structure of input paths, and incorporating each additional path type will significantly increase the required system memory and sampling time for HGNNs. To address these limitations, we introduce CompNode, a novel framework based on a new unified path schema definition called Complex-path, which is used to describe all the required path types, including nonlinear conditional path types. Then, we design a pre-aggregation method to reduce the required system memory and sampling time by pre-aggregating the same type of complex-path. Furthermore, we develop a model that combines semantic information from all aggregated complex-paths for accurate node ranking. Real-world experiments on identifying top potential high-value customers show CompNode outperforms state-of-the-art HGNNs by 20% in average precision and the previously deployed graph database method by 252% in success rate.

In the field of various downstream tasks of graph learning, graph neural networks (GNNs) have achieved the state-of-the-art (SOTA) performance benefits from its special propagation mechanism. The propagation mechanism aggregates attributes from neighbor nodes to obtain expressive node representations, which is pivotal for achieving SOTA performance in various downstream tasks. However, in most graph datasets, the neighborhood of each node may contain weakly correlated neighbors (WCNs), whose attributes may impair the expressiveness of central node representations. Though efforts have been devoted to solving such problem, they merely focus on aggregating fewer or even subtracting the attributes of WCNs. However, WCNs still share some correlated information with the central node, thus the correlated information provided by WCNs is underutilized. In this work, we devote to leveraging the correlated information provided by WCNs with our proposed method, namely GNN-detective. This detective can efficiently and automatically distinguish WCNs, as well as dig out their correlated information in the graph. It is realized by a semi-supervised learning framework, where the Differential Propagation (DP) module is designed specially for information triage and utilization. This module can fully leverage the correlated information provided by WCNs, and eliminate interference of uncorrelated information. We have conducted semi-supervised node classification tasks on 9 benchmark datasets. Our proposed method is proven to achieve the best performance in processing WCNs. The problems such as over-smoothing and overfitting are also mitigated as evaluated.