Pre-trained Graph Neural Networks (GNNs) have demonstrated remarkable performance in graph mining tasks, yet they often amplify societal biases against protected demographic groups. Existing fairness-aware approaches primarily address discrimination based on a single sensitive attribute (e.g., gender or race), overlooking real-world scenarios where individuals possess multiple overlapping demographic characteristics, leading to unfair treatment of underrepresented subgroups. Moreover, incorporating extra fairness constraints into pre-trained GNNs usually requires full model retraining, which is computationally expensive and often impractical. To address these limitations, we propose a novel Multi-attribute Fairness-aware Prompt Tuning framework named MultiFPT. Our approach operates in two key stages: in the graph prompt learning stage, MultiFPT injects fairness-aware structural and feature prompts into pre-trained GNN inputs; in the adapter tuning stage, a lightweight adapter regularized by the Hilbert–Schmidt Independence Criterion (HSIC) enforces statistical independence between node representations and multiple sensitive attributes. Experiments on real-world datasets demonstrate that MultiFPT significantly improves multi-attribute fairness, reducing bias by approximately 30% on average in node classification while maintaining competitive predictive performance compared to state-of-the-art baselines.

Abstract Short text classification has been a fundamental task in natural language processing, which benefits various applications, such as sentiment analysis, news tagging, and intent recommendation. However, classifying short texts is challenging due to the information sparsity in the text corpus. Besides, the performance of existing machine learning classification models largely relies on sufficient training data, yet labels can be scarce and expensive to obtain in real‐world text classification scenarios. In this article, we propose a novel self‐supervised short text classification method. Specifically, we first model the short text corpus as a heterogeneous graph to address the information sparsity problem. Then, we introduce a self‐attention‐based heterogeneous graph neural network model to learn short text embeddings. In addition, we adopt a self‐supervised learning framework to exploit internal and external similarities among short texts. Experiments on five real‐world short text benchmarks validate the effectiveness of our proposed method compared with the state‐of‐the‐art methods.

Learning robust representations for nodes in graphs is crucial for graph learning tasks. Graph Neural Networks(GNNs) attract much attention recently as the frameworks achieve great success in node representation learning. Existing state-of-the-art GNN methods (like GCN) aggregate messages from neighbor nodes through message passing neural network to update representations for nodes. However, the message passing strategy fails to capture the structural similarity between nodes. Besides, it assumes that neighbor nodes are independent and ignores abundant local neighbor structures around nodes in real networks. This weakness may hurt the performance of GNNs in some classification tasks. To capture the overlooked information, in the experimental investigation, we found that co-occurrence probabilities based on random walks can preserve local neighbor structures among nodes well. Furthermore, we propose a novel but effective method to encode local structure information into node features by co-occurrence probabilities. We call this method Local Structure Enhanced Graph Neural Network, short as LSEGNN. Extensive experiments are conducted in benchmark datasets and the results show the effectiveness of our method.

Graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly. Most GNNs assume that the networks are homophilous, which is not always true in the real world, i.e., structure noise or disassortative graphs. Only a few works focus on generalizing graph neural networks to heterophilous or low homophilous networks, where connected nodes may have different labels. In this paper, we design a simple and effective Graph Structure Learning strategy based on Feature and Label consistency (GSLFL) to increase the homophilous level of networks for generalizing any existing GNNs to heterophilous networks. Specifically, we first introduce a method to learn graph structure based on node features and then modify the graph structure based on label consistency. Further, we combine the GSLFL with three existing GNNs to learn node representations and graph structure together. And we design a self-training method to iteratively train models and modify graph structure with pseudo-labels. Finally, our empirical results on 6 public networks with homophily or heterophily, and structure attacks show that our methods outperform the state-of-the-art methods in most cases.

Graph Neural Networks (GNNs) have achieved significant success in handling graph-structured data, such as knowledge graphs, citation networks, molecular structures, etc. However, most of them are usually shallow structures because of the over-smoothing problem that the representations of nodes are indistinguishable when stacking many layers. Several recent studies have tried to design deep GNNs for powerful expression ability by enlarging the receptive fields to aggregate information from high-order neighbors. But deep models may give rise to overfitting problem. In this paper, we propose a novel insight to aggregate more useful information based on multi-view which does not require deep structures. Specifically, we first design two complementary views to describe global topology and feature similarity of nodes. Then we devise an attention strategy to fuse node representations, named M ulti-V iew G raph C onvolutional N etowrk(MV-GCN). Further, we introduce a self-supervised technique to learn node representations by contrastive learning on different views, which can learn distinctive node embeddings from a large number of unlabeled data, named M ulti-V iew C ontrastive G raph C onvolutional Network(MV-CGC). Finally, we conduct extensive experiments on six public datasets for node classification, which prove the superiority of two proposed models compared with state-of-the-art methods.

Graph Neural Networks (GNNs) are powerful tools for modeling graph-structured data to solve the tasks such as node classification, link prediction along with graph classification. For the graph classification task, properly defining the pooling strategies to vary the size and structure of the input graph, is of vital importance to generate a graph-level representation of the input graph. However, the existing GNN models usually fail to effectively capture the graph substructure information in pooling process. Besides, the importance of nodes(supernodes) within a graph has not been well-reflected. To remedy these limitations, we propose Gated Structure Aware Pooling (GSAPool), a sparse and differentiable pooling method, which focuses on retaining the graph substructure information during the process of pooling in an end-to-end fashion. Specifically, GSAPool utilizes dual gates along with a self-attention network to integrate the local structure to form clusters' embeddings. It also employs a novel formulation to capture the importance of each node/supernode in an input graph. Experiment results show that GSAPool achieves competitive graph classification performance over the state-of-the-art graph representation learning methods.