Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
Topology Adaptive Graph Convolutional Networks
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network defined in the vertex domain. We provide a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution. The TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing. Since no approximation to the convolution is needed, TAGCN exhibits better performance than existing spectral CNNs on a number of data sets and is also computationally simpler than other recent methods.
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cs.LG 2representative citing papers
PFΔ is a benchmark dataset of 859,800 power flow solutions across six bus system sizes with N/N-1/N-2 contingencies and close-to-infeasible cases to evaluate traditional solvers and GNN methods.
citing papers explorer
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Learning Dynamic Stability Landscapes in Synchronization Networks
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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PF$\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
PFΔ is a benchmark dataset of 859,800 power flow solutions across six bus system sizes with N/N-1/N-2 contingencies and close-to-infeasible cases to evaluate traditional solvers and GNN methods.