Subgraph Federated Learning (FL) enables collaborative training of Graph Neural Networks (GNNs) across clients, each holding a local subgraph. However, it faces significant challenges due to label scarcity and community heterogeneity. We propose a two-stage framework that combines self-supervised pre-training with personalized fine-tuning to address these issues. As the first to extend graph self-supervised learning to the FL setting, our FedEdge method enables clients to collaboratively learn meaningful structural representations without labeled data. In the fine-tuning stage, personalized aggregation is guided by functional similarity, promoting collaboration among clients from similar communities. Extensive experiments on three real-world datasets show that FedEdge consistently outperforms five state-of-the-art methods, improving average test accuracy by up to 9.86% over the best FL baseline.

Graph neural networks (GNNs) have attracted extensive research attention in recent years due to their capability to progress with graph data and have been widely used in practical applications. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in federated learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated GNNs (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this article, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-D taxonomy of the FedGNN literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNN training and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

Graph neural networks (GNNs) have achieved promising results for node classification tasks. However, due to the memorization effects, GNNs easily overfit in the presence of label noise. Currently, this problem remains open. In this paper, we propose CLaSS to bridge this important gap. It is a first-of-its-kind sample selection-based framework for training noise-resistant GNNs. Specifically, CLaSS performs graph contrastive learning to improve the robustness of GNNs and effectively leverages unlabeled nodes. To mitigate the confirmation bias, two GNNs are trained through co-teaching to select true-labeled nodes and filter out noisy nodes for each other. CLaSS also performs label refinement on selected clean nodes and guessing on selected noisy ones, with the help of node clustering. Extensive experiments on four real-world datasets demonstrate significant advantages of CLaSS over five state-of-the-art approaches.

Bike sharing systems are popular worldwide now. However, these systems are facing a problem - rebalancing of shareable bikes among different docking stations. To address this challenge, we propose an approach for the spatial-temporal rebalancing of shareable bikes which allows domain experts to optimize the rebalancing operation with their knowledge and preferences without relying on learning by trial-and-error.

Order dispatch is an important area where artificial intelligence (AI) can benefit ride-sharing systems (e.g., Grab, Uber), which has become an integral part of our public transport network. In this paper, we present a multi-agent testbed to study the spread of infectious diseases through such a system. It allows users to vary the parameters of the disease and behaviours to study the interaction effect between technology, disease and people's behaviours in such a complex environment.