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cs.LG2024
A Benchmark on Directed Graph Representation Learning in Hardware Designs
Haoyu Wang, Yinan Huang, Nan Wu +1
To keep pace with the rapid advancements in design complexity within modern computing systems, directed graph representation learning (DGRL) has become crucial, particularly for en…
cs.LG2024★ 1 cited
What Are Good Positional Encodings for Directed Graphs?
Yinan Huang, Haoyu Wang, Pan Li
Positional encodings (PEs) are essential for building powerful and expressive graph neural networks and graph transformers, as they effectively capture the relative spatial relatio…
cs.LG2024★ 1 cited
What Can We Learn from State Space Models for Machine Learning on Graphs?
Yinan Huang, Siqi Miao, Pan Li
Machine learning on graphs has recently found extensive applications across domains. However, the commonly used Message Passing Neural Networks (MPNNs) suffer from limited expressi…