Graph Representation Learning via Adaptive Expectile Aggregation introduces ExpectileGCN, a graph neural network framework that learns an expectile level to adaptively aggregate neighborhood information beyond simple mean pooling. The method smoothly interpolates between minimum- and maximum-type behaviors, with mean aggregation as a special case, and employs a differentiable sigmoid‑based approximation for end‑to‑end training. The authors provide theoretical stability and generalization guarantees, analyze classification risk under heterophilic settings, and demonstrate improved performance and robustness on both homophilic and heterophilic benchmark graphs.
arXiv:2601. 19449v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations.
arXiv:2608. 14823v1 Announce Type: new Abstract: Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare?
arXiv:2609.08685v1 Announce Type: new Abstract: Standard open-set node classification methods rely on the homophily assumption, where connected nodes share labels. However, real-world graphs are ofte...
arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.
arXiv:2505.11298v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive per...
HERALD is a new gradient‑free graph condensation framework that adapts node scoring and feature selection to a graph’s heterophily level. It selects features using a joint Fisher‑discriminability and activation‑density criterion, and scores nodes with a weighted combination of prototype representativeness, decision‑boundary proximity, and Local Intrinsic Dimensionality, where the weights depend on the heterophily ratio. The selected nodes are assembled into a condensed subgraph via score‑ordered BFS expansion, Personalized PageRank pruning, and class rebalancing, achieving comparable storage to BONSAI and outperforming state‑of‑the‑art condensers on heterophilic graphs while remaining competitive on homophilic ones across multiple GNN architectures.
Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically...
SCARCE is a label‑free representation‑learning framework that improves graph‑level out‑of‑distribution generalization. It uses an environment partition of training graphs—derived from metadata or self‑supervised clustering—to warm‑start a variational graph autoencoder and then applies an adversarial confusion mechanism that prevents a discriminator from distinguishing environments while preserving reconstruction fidelity. Theoretical analysis shows that this environment confusion contracts environment‑discriminative directions, effectively reweighting features to reduce reliance on spurious correlations, and experiments on synthetic and DrugOOD datasets demonstrate state‑of‑the‑art OOD performance among label‑free methods.
arXiv:2607. 11577v1 Announce Type: cross Abstract: We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model.
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.
arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.