REVIEW 5 cited by
Hierarchical Graph Pooling with Structure Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing GNN models mainly focus on designing graph convolution operations. The graph pooling (or downsampling) operations, that play an important role in learning hierarchical representations, are usually overlooked. In this paper, we propose a novel graph pooling operator, called Hierarchical Graph Pooling with Structure Learning (HGP-SL), which can be integrated into various graph neural network architectures. HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs. More specifically, the graph pooling operation adaptively selects a subset of nodes to form an induced subgraph for the subsequent layers. To preserve the integrity of graph's topological information, we further introduce a structure learning mechanism to learn a refined graph structure for the pooled graph at each layer. By combining HGP-SL operator with graph neural networks, we perform graph level representation learning with focus on graph classification task. Experimental results on six widely used benchmarks demonstrate the effectiveness of our proposed model.
Forward citations
Cited by 5 Pith papers
-
The Generalized Skew Spectrum of Graphs
The generalized Skew Spectrum embeds attributed, multilayer, and hypergraph data into permutation-invariant vectors, and its doubly-reduced k-spectra distinguish all 7-node graphs at the same asymptotic cost as the or...
-
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
MetaGMT applies a bi-level meta-learning objective to GMT, improving explanation edge-fidelity on several benchmarks while showing accuracy drops under high spurious bias.
-
Graph Structure Refinement with Energy-based Contrastive Learning
A new framework, ECL-GSR, uses energy-based contrastive learning to refine noisy graph structure and reports state-of-the-art node classification accuracy on eight benchmarks.
-
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
A graph neural network using histology context predicts neoadjuvant chemotherapy response in triple negative breast cancer with 90% accuracy on a 105-patient internal cross-validation.
-
Towards Data-centric Machine Learning on Directed Graphs: a Survey
A survey taxonomizing directed graph neural networks into message-passing, eigenpolynomial, and sequence-based frameworks and re-reading them from a data-centric perspective.
Discussion (0). Continue with ORCID to comment.