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This content will become publicly available on July 13, 2026

Title: Implicit Subgraph Neural Network
Subgraph neural networks have recently gained prominence for subgraph-level predictive tasks, but existing methods either use simple pooling over graph convolutional networks that fail to capture essential subgraph properties or rely on rigid subgraph definitions that limit performance; moreover, they cannot model long-range dependencies between and within subgraphs—an important limitation given real-world networks’ diverse structures. To address this, we propose the first implicit subgraph neural network that captures dependencies across subgraphs and integrates label-aware subgraph-level information, formulating implicit subgraph learning as a bilevel optimization problem and introducing a provably convergent algorithm requiring fewer gradient estimations than standard bilevel methods, achieving superior performance on real-world benchmarks.  more » « less
Award ID(s):
2306331
PAR ID:
10644449
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Forty-second International Conference on Machine Learning
Date Published:
Format(s):
Medium: X
Location:
Vancouver, Canada
Sponsoring Org:
National Science Foundation
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