Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering
CoCo framework captures compactness and consistency in graph neural network representations for improved deep graph clustering.
Avg rating: 6.80 (4–8) · Wei Ju et al.
Graph Learning
Graph neural networks, structured prediction on graphs, graph theory, knowledge graphs.
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Exchangeability of GNN Representations with Applications to Graph Retrieval
Graph embeddings exhibit exchangeability property, enabling efficient graph retrieval via transport-based similarity approximation with locality-sensitive hashing.
Avg rating: 6.00 (4–8) · Kartik Nair et al.
Learning with Dual-level Noisy Correspondence for Multi-modal Entity Alignment
Proposes framework to handle noisy entity-attribute and inter-graph correspondences in multi-modal entity alignment.
Avg rating: 7.50 (6–8) · Haobin Li et al.
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
GraphGlue uses Riemannian geometry to merge multi-domain graphs into unified manifolds, enabling knowledge transfer across graph domains.
Avg rating: 6.00 (4–8) · Li Sun et al.
One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
Unified framework for imbalanced graph classification using dynamic balanced prototypes and prototype load-balancing optimization.
Avg rating: 5.50 (4–6) · Guanjun Wang et al.
Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
Analyzes scaling laws for shallow networks with feature learning via sparse estimation and matrix compression theory.
Avg rating: 7.00 (6–8) · Leonardo Defilippis et al.