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Found 5 papers, 2 papers with code
HeroLT: Benchmarking Heterogeneous Long-Tailed Learning
To achieve this, we develop the most comprehensive (to the best of our knowledge) long-tailed learning benchmark named HeroLT, which integrates 13 state-of-the-art algorithms and 6 evaluation metrics on 14 real-world benchmark datasets across 4 tasks from 3 domains.
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect the prediction performance of graph neural networks (GNNs)?
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
To bridge this gap, we propose a generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i. e., each task corresponds to the prediction of one particular class.
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
To answer it, we propose a generalization bound for dynamic non-IID transfer learning on graphs, which implies the generalization performance is dominated by domain evolution and domain discrepancy between source and target graphs.
A Benchmark for Federated Hetero-Task Learning
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.
