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arXiv Statistics ML By Tianyi Zhu, Wei Lan

Graph Representation Learning via Adaptive Expectile Aggregation

Read the original on arXiv Statistics ML →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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