Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and heterophily, where connected nodes may have dissimilar labels and attributes. However, existing benchmarks primarily focus on either heterophilic homogeneous graphs or homophilic heterogeneous graphs, leaving a significant gap in understanding how models perform on graphs with both heterogeneity and heterophily. To bridge this gap, we introduce H2GB, a large-scale node-classification graph benchmark that brings together the complexities of both the heterophily and heterogeneity properties of real-world graphs. H2GB encompasses 9 real-world datasets spanning 5 diverse domains, 28 baseline models, and a unified benchmarking library with a standardized data loader, evaluator, unified modeling framework, and an extensible framework for reproducibility. We establish a standardized workflow supporting both model selection and development, enabling researchers to easily benchmark graph learning methods. Extensive experiments across 28 baselines reveal that current methods struggle with heterophilic and heterogeneous graphs, underscoring the need for improved approaches. Finally, we present a new variant of the model, H2G-former, developed following our standardized workflow, that excels at this challenging benchmark. Both the benchmark and the framework are publicly available at Github and PyPI, with documentation hosted at https://junhongmit.github.io/H2GB.

Predictive Maintenance (PDM) systems are essential for preemptive monitoring of sensor signals to detect potential machine component failures in industrial assets such as bearings in rotating machinery. Existing PDM systems face two primary challenges: 1) Irregular Signal Acquisition, where data collection from the sensors is intermittent, and 2) Signal Heterogeneity, where the full spectrum of sensor modalities is not effectively integrated. To address these challenges, we propose a Curriculum Learning Framework for Multi-Modal Predictive Maintenance - MentorPDM. MentorPDM consists of 1) a graph-augmented pretraining module that captures intrinsic and structured temporal correlations across time segments via a temporal contrastive learning objective and 2) a bi-level curriculum learning module that captures task complexities for weighing the importance of signal modalities and samples via modality and sample curricula. Empirical results from MentorPDM show promising performance with better generalizability in PDM tasks compared to existing benchmarks. The efficacy of the MentorPDM model will be further demonstrated in real industry testbeds and platforms.

Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily and spectral filters is more complex - the optimal filter response varies across frequency components and does not follow a strict monotonic correlation with heterophily degree. This finding challenges conventional fixed filter designs and suggests the need for adaptive filtering to preserve expressiveness in graph embeddings. Formally, natural questions arise: Given a heterophilic graph $\mathcal{G}$ , how and to what extent will the varying heterophily degree of $\mathcal{G}$ affect the performance of GNNs? How can we design adaptive filters to fit those varying heterophilic connections? Our theoretical analysis reveals that the average frequency response of GNNs and graph heterophily degree do not follow a strict monotonic correlation, necessitating adaptive graph filters to guarantee good generalization performance. Hence, we propose HeroFilter, a simple yet powerful GNN, which extracts information across the heterophily spectrum and combines salient representations through adaptive mixing. HeroFilter's superior performance achieves up to 9.2% accuracy improvement over leading baselines across homophilic and heterophilic graphs.

Graph learning plays a pivotal role in many high-impact application domains. Despite significant advances in this field, there is currently no single solution that effectively handles (1) data heterogeneity, (2) long-range dependencies, (3) graph heterophily, and (4) scalability to large graphs, all at the same time. Classical graph neural networks (GNNs) and graph transformers (GTs) address some but not all of these issues, often suffering from limitations such as quadratic computation complexity and/or suboptimal generalization performance in realistic applications. This paper introduces UnifiedGT, a novel framework that systematically addresses all of these challenges by automatically providing a graph transformer architecture with multiple components via neural architecture search. UnifiedGT consists of five major components: (1) graph sampling, (2) structural prior injection, (3) graph attention, (4) local/global information mixing, and (5) type-specific feedforward networks (FFNs). This modular approach enables the efficient processing of large-scale graphs and effective management of heterogeneity and heterophily while capturing long-range dependencies. We demonstrate the versatility of UnifiedGT through comprehensive experiments on several benchmark datasets, revealing insights such as the efficacy of graph sampling for GTs, the importance of explicit graph structure injection via attention masking, and the synergistic effect of local/global information mixing via a combination of global attention with local message passing. Furthermore, we formulate these design choices into a search space, where an optimal combination can be discovered for a particular dataset via neural architecture search. Notably, UnifiedGT improves generalization performance on various graph datasets, outperforming state-of-the-art GT models by a margin of about 3.7% on average. The framework is available on Github (https://github.com/junhongmit/H2GB) and PyPI (https://pypi.org/project/H2GB/), and documentation can be found at https://junhongmit.github.io/H2GB/.

The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy. However, current PFL techniques primarily focus on modeling the conditional distribution heterogeneity (i.e. concept shift), which can result in suboptimal performance when the distribution of input data across clients diverges (i.e. covariate shift). Additionally, these techniques often lack the ability to adapt to unseen data, further limiting their effectiveness in real-world scenarios. To address these limitations, we propose a novel approach, FedGMM, which utilizes Gaussian mixture models (GMM) to effectively fit the input data distributions across diverse clients. The model parameters are estimated by maximum likelihood estimation utilizing a federated Expectation-Maximization algorithm, which is solved in closed form and does not assume gradient similarity. Furthermore, FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.

Learning on graphs (LOG) plays a pivotal role in various high-impact application domains. The past decades have developed tremendous theories, algorithms, and open-source systems in answering what/who questions on graphs. However, recent studies reveal that the state-of-the-art techniques for learning on graphs (LOG) are often not trustworthy in practice with respect to several social aspects (e.g., fairness, transparency, security). A natural research question to ask is: how can we make learning algorithms on graphs trustworthy? To answer this question, we propose a paradigm shift, from answering what and who LOG questions to understanding how and why LOG questions. The TrustLOG workshop provides a venue for presenting, discussing, and promoting frontier research on trustworthy learning on graphs. Moreover, TrustLOG will serve as an impulse for the LOG community to identify novel research problems and shed new light on future directions.