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This study presents the HGLA-Pool method, an innovative approach for hierarchical graph representation. It employs a two-fold strategy to capture the graph's ...
Graph pooling is an essential operation in Graph Neural Networks that reduces the size of an input graph while preserving its core structural properties.
In this work, we contribute to solve the community detection problem by proposing an algorithm for the detection of disjoint communities' cores considering ...
Graph pooling is an essential operation in Graph Neural Networks that reduces the size of an input graph while preserving its core structural properties.
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M. Simonovsky, N. Komodakis, Dynamic edge-conditioned filters in convolutional neural networks on graphs, in: Proceedings of the IEEE Conference on Computer ...
We have proposed a novel GNN based graph classification algorithm called SubGattPool which uses higher order atten- tion over the subgraphs of a graph and also ...
May 16, 2024 · We adopt four hierarchical pooling methods as baselines for comparisons, including the Self-Attention Graph Pooling with the hierarchical ...
One important operation for graph classification tasks is downsampling or pooling, which obtains graph representations from node representations. However, most ...
Jun 7, 2023 · Within GNNs, a pooling operation reduces the size of the input graph by grouping nodes that share commonalities intending to generate more ...
We propose Master2Token (M2T), a new self-attention framework which is better suited for global tasks like pooling. • We introduce a new convolution operator ...