WebNov 28, 2024 · Generalized Few-Shot Node Classification Abstract: For real-world graph data, the node class distribution is inherently imbalanced and long-tailed, which naturally … WebJun 12, 2024 · Though meta-learning has been applied to different few-shot graph learning problems, most existing efforts predominately assume that all the data from those seen classes is gold-labeled, while those methods …
Yuan FANG @ SMU - Publications
WebFew-shot node classification on attributed networks is gradually becoming a research hotspot. Although several methods aim to integrate meta-learning with graph neural networks to address this problem, some limitations remain. First, they all assume node representation learning using graph neural networks in homophilic graphs. WebJan 8, 2024 · Moreover, different architectures and learning algorithms make it difficult to study the effectiveness of existing 2D methods when migrating to the 3D domain.In this … farmhouse\u0027s g8
InfoMax Classification-Enhanced Learnable Network for Few …
WebJan 3, 2024 · The contributions of this paper are the following: A new few-shot node classification framework (ICELN) is proposed, where we em- phasize learning task-specific classifiers from a limited number of labeled nodes and transfer the discriminative class characteristics to unlabeled nodes. WebApr 10, 2024 · To attack this challenge, we first put forth MetaRF, an attention-based random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while … http://www.ece.virginia.edu/~jl6qk/pubs/CIKM2024-2.pdf farmhouse\u0027s gg