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cs.LG2025
Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices
Qingyu Han, Qian Li, Linxin Yang +3
Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as a promising paradigm, with Grap…
cs.LG2025
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
Senmiao Wang, Yupeng Chen, Yushun Zhang +2
Graph Neural Networks (GNNs) often suffer from performance degradation as the network depth increases. This paper addresses this issue by introducing initialization methods that en…
cs.LG2025
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
Qian Chen, Lei Li, Qian Li +6
A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emer…