A Topological Perspective on Demystifying GNN-Based Link Prediction Performance

Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs, none have explored its varying performance across different nodes and its underlying reasons. To this end, we aim to demystify which nodes will perform better from the perspective of their local topology. Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors. We empirically demonstrate that TC has a higher correlation with LP performance than other node-level topological metrics like degree and subgraph density, offering a better way to identify low-performing nodes than using cold-start. With TC, we discover a novel topological distribution shift issue in which newly joined neighbors of a node tend to become less interactive with that node's existing neighbors, compromising the generalizability of node embeddings for LP at testing time. To make the computation of TC scalable, We further propose Approximated Topological Concentration (ATC) and theoretically/empirically justify its efficacy in approximating TC and reducing the computation complexity. Given the positive correlation between node TC and its LP performance, we explore the potential of boosting LP performance via enhancing TC by re-weighting edges in the message-passing and discuss its effectiveness with limitations. Our code is publicly available at https://github.com/YuWVandy/Topo_LP_GNN.

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

Fairness and Diversity in Recommender Systems: A Survey

Recommender systems are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to increasing attention to fairness-aware and diversity-aware recommender systems. While most existing studies explore fairness and diversity independently, we identify strong connections between these two domains. In this survey, we first discuss each of them individually and then dive into their connections. Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level. With this expanded perspective on user and item-level diversity, we re-interpret fairness studies from the viewpoint of diversity. This fresh perspective enhances our understanding of fairness-related work and paves the way for potential future research directions. Papers discussed in this survey along with public code links are available at https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems .

Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations

While machine learning models have achieved unprecedented success in real-world applications, they might make biased/unfair decisions for specific demographic groups and hence result in discriminative outcomes. Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure. This results in their inability to capture procedure-oriented bias, which therefore limits the ability to have a fully debiasing method. Fortunately, with the rapid development of explainable machine learning, explanations for predictions are now available to gain insights into the procedure. In this work, we bridge the gap between fairness and explainability by presenting a novel perspective of procedure-oriented fairness based on explanations. We identify the procedure-based bias by measuring the gap of explanation quality between different groups with Ratio-based and Value-based Explanation Fairness. The new metrics further motivate us to design an optimization objective to mitigate the procedure-based bias where we observe that it will also mitigate bias from the prediction. Based on our designed optimization objective, we propose a Comprehensive Fairness Algorithm (CFA), which simultaneously fulfills multiple objectives - improving traditional fairness, satisfying explanation fairness, and maintaining the utility performance. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed CFA and highlight the importance of considering fairness from the explainability perspective. Our code is publicly available at https://github.com/YuyingZhao/FairExplanations-CFA .

Collaboration-Aware Graph Convolutional Networks for Recommendation Systems

By virtue of the message-passing that implicitly injects collaborative effect into the embedding process, Graph Neural Networks (GNNs) have been successfully adopted in recommendation systems. Nevertheless, most of existing message-passing mechanisms in recommendation are directly inherited from GNNs without any recommendation-tailored modification. Although some efforts have been made towards simplifying GNNs to improve the performance/efficiency of recommendation, no study has comprehensively scrutinized how message-passing captures collaborative effect and whether the captured effect would benefit the prediction of user preferences over items. Therefore, in this work we aim to demystify the collaborative effect captured by message-passing in GNNs and develop new insights towards customizing message-passing for recommendation. First, we theoretically analyze how message-passing captures and leverages the collaborative effect in predicting user preferences. Then, to determine whether the captured collaborative effect would benefit the prediction of user preferences, we propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighborhood set. Inspired by our theoretical and empirical analysis, we propose a recommendation-tailored GNN, Augmented Collaboration-Aware Graph Convolutional Network (CAGCN*), that extends upon the LightGCN framework and is able to selectively pass information of neighbors based on their CIR via the Collaboration-Aware Graph Convolution. Experimental results on six benchmark datasets show that CAGCN* outperforms the most representative GNN-based recommendation model, LightGCN, by 9% in Recall@20 and also achieves more than 79% speedup. Our code is publicly available at https://github.com/YuWVandy/CAGCN.

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Although some work has developed fair GNNs, most of them directly borrow fair representation learning techniques from non-graph domains without considering the potential problem of sensitive attribute leakage caused by feature propagation in GNNs. However, we empirically observe that feature propagation could vary the correlation of previously innocuous non-sensitive features to the sensitive ones. This can be viewed as a leakage of sensitive information which could further exacerbate discrimination in predictions. Thus, we design two feature masking strategies according to feature correlations to highlight the importance of considering feature propagation and correlation variation in alleviating discrimination. Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation. Given the learned fair views, we adaptively clamp weights of the encoder to avoid using sensitive-related features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a better trade-off between model utility and fairness. Our code is publicly available at https://github.com/YuWVandy/FairVGNN.

Imbalanced Graph Classification via Graph-of-Graph Neural Networks

Graph Neural Networks (GNNs) have achieved unprecedented success in learning graph representations to identify categorical labels of graphs. However, most existing graph classification problems with GNNs follow a balanced data splitting protocol, which is misaligned with many real-world scenarios in which some classes have much fewer labels than others. Directly training GNNs under this imbalanced situation may lead to uninformative representations of graphs in minority classes, and compromise the overall performance of downstream classification, which signifies the importance of developing effective GNNs for handling imbalanced graph classification. Existing methods are either tailored for non-graph structured data or designed specifically for imbalance node classification while few focus on imbalance graph classification. To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from graphs themselves. Globally, we construct a graph of graphs (GoG) based on kernel similarity and perform GoG propagation to aggregate neighboring graph representations, which are initially obtained by node-level propagation with pooling via a GNN encoder. Locally, we employ topological augmentation via masking nodes or dropping edges to improve the model generalizability in discerning topology of unseen testing graphs. Extensive graph classification experiments conducted on seven benchmark datasets demonstrate our proposed G$^2$GNN outperforms numerous baselines by roughly 5\% in both F1-macro and F1-micro scores. The implementation of G$^2$GNN is available at \href{https://github.com/YuWVandy/G2GNN}{https://github.com/YuWVandy/G2GNN}.