Weband language, e.g., [39, 51, 27]. However, meta learning on graphs has received considerably less research attention and has remained a problem beyond the reach of … WebOct 30, 2024 · Graph Meta Learning via Local Subgraphs. arXiv preprint arXiv:2006.07889 (2024). Google Scholar; Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, …
Graph Meta Learning via Local Subgraphs - arxiv.org
WebHeterogeneous Graph Contrastive Learning with Meta-path Contexts and Weighted Negative Samples Jianxiang Yu∗ Xiang Li ∗† Abstract Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic re- WebNov 25, 2024 · Knowledge-graph based Proactive Dialogue Generation with Improved Meta-learning. Pages 40–46. ... Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel .2024. Meta-learning with temporal convolutions. arXiv preprint arXiv:1707.03141, 2(7). Google Scholar; Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and … noths supplier site
HG-Meta: Graph Meta-learning over Heterogeneous Graphs
WebApr 7, 2024 · Abstract. In this paper, we propose a self-distillation framework with meta learning (MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the long-tail samples. Specifically, we first propose a dynamic pruning technique to obtain a small pruned model from a large … WebFeb 22, 2024 · Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve … WebHeterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings.Thus, in this paper, we propose an Attributed Multi-Order Graph ... noths seller