May 27, 2024 · We propose open-world graph condensation (OpenGC), a robust GC framework that integrates structure-aware distribution shift to simulate evolving graph patterns.
Aug 24, 2024 · We propose open-world graph condensation (OpenGC), a robust GC framework that integrates structure-aware distribution shift to simulate evolving graph patterns.
ABSTRACT. The burgeoning volume of graph data presents significant com- putational challenges in training graph neural networks (GNNs),.
We are the first to study the open-world graph learning and propose an novel uncertain node representation learning approach, based on a variant of GCN (i.e., ...
Jul 15, 2024 · KDD 2024 - Graph Condensation for Open-World Graph Learning. 53 views · 4 months ago ...more. Try YouTube Kids. An app made just for kids. Open ...
Sep 13, 2024 · This approach is designed to extract temporal invariant patterns from the original graph, thereby enhancing the generalization capabilities of ...
It tackles the challenge of minimizing information loss during the condensation process. ... Contrastive Graph Condensation: Advancing Data Versatility through ...
This is a curated list of papers about graph reduction including graph condensation, graph coarsening, graph sparsification, graph summarization, etc.
Jun 12, 2024 · Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning. Xinyi Gao, Yayong Li, Tong Chen, Guanhua Ye, Wentao ...
Graph condensation (GC) is a data-centric approach that accelerates GNN model training by creating a compact yet representative graph to replace the ...