تقرير
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
| العنوان: | SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks |
|---|---|
| المؤلفون: | Morris, Christopher, Rattan, Gaurav, Kiefer, Sandra, Ravanbakhsh, Siamak |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning |
| الوصف: | While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graphs. They either operate on $k$-order tensors or consider all $k$-node subgraphs, implying an exponential dependence on $k$ in memory requirements, and do not adapt to the sparsity of the graph. By introducing new heuristics for the graph isomorphism problem, we devise a class of universal, permutation-equivariant graph networks, which, unlike previous architectures, offer a fine-grained control between expressivity and scalability and adapt to the sparsity of the graph. These architectures lead to vastly reduced computation times compared to standard higher-order graph networks in the supervised node- and graph-level classification and regression regime while significantly improving over standard graph neural network and graph kernel architectures in terms of predictive performance. Comment: ICML 2022, fixed typo in Observation 1 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.13913 |
| رقم الأكسشن: | edsarx.2203.13913 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |