In real-world scenarios, most complex systems can be generally modelled as homogenous or heterogenous networks. Therefore, downstream tasks (e.g., node/graph classification, node clustering) based on these two types of graphs become ubiquitous and have drawn considerable attentions in recent years. Existing literatures on node classification mainly focuses on either homogeneous or heterogeneous graphs, while research on effectively carrying out node classification tasks on both types of graphs simultaneously still under-exploited. To fill this gap, we propose a universal Graph Neural Network architecture based on Subgraph and Subhypergraph (SS-GNN) with feature-enhanced strategy for node embedding on both homogeneous and heterogeneous graphs. Through construction of subgraph and subhypergraph with same-class nodes, our model can simultaneously deal with homogeneous and heterogeneous graphs. Graph attention modules are especially designed to embed subgraphs of same-class nodes to learn the internal topological structure and local community structure within the original graph. Additionally, to capture high-order features of graph and enhance the embedding representations of nodes, we also utilize hypergraph attention modules to embed subhypergraphs of same-class nodes. Unlike other approaches that rely on pre-defined meta-paths, our model can be readily applied to most real-world applications without requiring any domain knowledge. Finally, we conduct extensive experiments on three homogeneous and three heterogeneous real-world graphs to demonstrate the effectiveness of SS-GNN. The experimental results for node classification and clustering tasks not only show the superior performance of our proposed model compared to state-of-the-art, but also demonstrate its potentially good interpretability for graph analysis. This work may provide some enlightening insights to the study on universality of graph foundation model.

Matrix factorization (MF) is a fundamental model in data mining and machine learning, which finds wide applications in diverse application areas, including recommendation systems with user-item rating matrices, phenotype extraction from electronic health records, and spatial-temporal data analysis for check-in records. The "right to be forgotten" has become an indispensable privacy consideration due to the widely enforced data protection regulations, which allow personal users having contributed their data for model training to revoke their data through a data deletion request. Consequently, it gives rise to the emerging task of machine unlearning for the MF model, which removes the influence of the matrix rows/columns from the trained MF factors upon receiving the deletion requests from the data owners of these rows/columns. The central goal is to effectively remove the influence of the rows/columns to be forgotten, while avoiding the computationally prohibitive baseline approach of retraining from scratch. Existing machine unlearning methods are either designed for single-variable models and not compatible with MF that has two factors as coupled model variables, or require alternative updates that are not efficient enough. In this paper, we propose a closed-form machine unlearning method. In particular, we explicitly capture the implicit dependency between the two factors, which yields the total Hessian-based Newton step as the closed-form unlearning update. In addition, we further introduce a series of efficiency-enhancement strategies by exploiting the structural properties of the total Hessian. Extensive experiments on five real-world datasets from three application areas as well as synthetic datasets validate the efficiency, effectiveness, and utility of the proposed method.

Spatiotemporal forecasting has been attracting tremendous interest in various fields, among which traffic flow prediction is a representative example. Existing methods typically deal with the complex spatial and temporal dependencies in traffic flow through graph neural networks (GNNs) and temporal neural networks (TNNs), respectively. However, these works still fall short due to: 1) deep GNNs have the over-smoothing problem that hinders the handling of higher-order spatial correlations; 2) TNNs have difficulty in extracting the temporal dependencies with different localities. To this end, this paper proposes Higher-Order Masked Graph Neural Networks (HOMGNNs) to model and predict the traffic flow data. Concretely, we design the spatial graph learning layer to adaptively characterize the dependency correlations of different orders, and the higher-order GNN (HOGNN) is further proposed to deal with these correlations. Furthermore, we define and construct a temporal graph to represent the temporal dynamics in traffic data. The masked GNN (MGNN) is further proposed to extract these dynamics based on the temporal graph. To validate the superiority of the proposed HOMGNNs, we conduct extensive experiments on METR-LA and PEMS-BAY datasets. Experimental results demonstrate the remarkable performance of our method compared with 11 state-of-the-art baselines.