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The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
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Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networks (GNNs) over traditional Neural Networks (NNs) on graph-structured data, especially on node-level tasks. However, recent work has identified a non-trivial set of datasets where GNN's performance compared to the NN's is not satisfactory. Heterophily, i.e. low homophily, has been considered the main cause of this empirical observation. People have begun to revisit and re-evaluate most existing graph models, including graph transformer and its variants, in the heterophily scenario across various kinds of graphs, e.g. heterogeneous graphs, temporal graphs and hypergraphs. Moreover, numerous graph-related applications are found to be closely related to the heterophily problem. In the past few years, considerable effort has been devoted to studying and addressing the heterophily issue. In this survey, we provide a comprehensive review of the latest progress on heterophilic graph learning, including an extensive summary of benchmark datasets and evaluation of homophily metrics on synthetic graphs, meticulous classification of the most updated supervised and unsupervised learning methods, thorough digestion of the theoretical analysis on homophily/heterophily, and broad exploration of the heterophily-related applications. Notably, through detailed experiments, we are the first to categorize benchmark heterophilic datasets into three sub-categories: malignant, benign and ambiguous heterophily. Malignant and ambiguous datasets are identified as the real challenging datasets to test the effectiveness of new models on the heterophily challenge. Finally, we propose several challenges and future directions for heterophilic graph representation learning.
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Cited by 12 Pith papers
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Revisiting Graph Homophily Measures
A new 'unbiased homophily' measure satisfies all five previously proposed reliability axioms for undirected graphs, and an impossibility proof shows no such measure exists for directed graphs.
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Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
ACE adds a heterophily-aware auxiliary loss to coarsening-based GNN training, recovering discarded node-level information and improving accuracy on heterophilic graphs by up to ~15 points.
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TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows
TANGO adds a learnable energy gradient and an orthogonal tangential flow to GNN layers, improving long-range and heterophilic graph benchmarks.
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Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
MDGCL pre-trains graph encoders on multiple domains using same-domain discrimination and a downstream domain-attention mechanism, outperforming existing text-free graph foundation models.
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Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery
BotHP combines a dual-encoder (graph and MLP) with prototype-guided clustering to pre-train graph bot detectors, improving F1 by 1.3-6.0 points on TwiBot-20 and MGTAB.
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Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
A message-passing GNN based on a linear recurrence plus MLP readout achieves strong results on long-range, heterophilic, and spatio-temporal graph benchmarks.
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Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
A partition-wise graph filtering method, CPF, unifies graph-wise and node-wise filtering and achieves state-of-the-art node classification on 13 benchmark graphs and anomaly detection on 3 datasets.
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Understanding GNNs and Homophily in Dynamic Node Classification
In dynamic node classification, GCN discriminative power is characterized by the probability that a node's future label matches its neighbors' current labels, which the authors formalize as dynamic homophily.
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GRAMA: Adaptive Graph Autoregressive Moving Average Models
GRAMA is a graph-adaptive ARMA architecture that wraps GNN backbones with selective sequential recurrences and reports consistent gains on long-range graph benchmarks.
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Personalized One-shot Federated Graph Learning for Heterogeneous Clients
O-pFGL achieves one-shot personalized federated graph learning by aggregating class-wise feature statistics into a surrogate graph and combining global distillation with local fine-tuning, outperforming baselines on 1...
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A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data
Graph neural networks are compiled into or connected to relational Bayesian networks, enabling MAP-based collective classification and multi-objective optimization on graphs.
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How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
SBM-style probabilistic models outperform graph neural networks on link prediction when node features are low-dimensional, noisy, or the graph is heterophilic.
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