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Found 5 papers, 3 papers with code
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Lastly, we provide a theoretical analysis to show that under a planted block model of tasks on graphs, our affinity scores can provably separate tasks into groups.
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Graph neural networks are widely used tools for graph prediction tasks.
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community Labeling
In this work, we provide an empirical and theoretical analysis for the performance of a class of embeddings on the common task of pairwise community labeling.
The impossibility of low rank representations for triangle-rich complex networks
The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science.
LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew
Theoretically, we show that LSF-Join efficiently finds most close pairs, even for small similarity thresholds and for skewed input sets.
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