Kdd 20 Tinygnn Learning Efficient Graph Neural Networks

и єж з и пјљkdd 20 Tinygnn Learning Efficient Graph Neural Networks зџґд ћ Tinygnn: learning efficient graph neural networks authors : bencheng yan , chaokun wang , gaoyang guo , yunkai lou authors info & claims kdd '20: proceedings of the 26th acm sigkdd international conference on knowledge discovery & data mining. Tinygnn: learning efficient graph neural networks. august 2020. doi: 10.1145 3394486.3403236. conference: kdd '20: the 26th acm sigkdd conference on knowledge discovery and data mining. authors.

и єж з и пјљkdd 20 Tinygnn Learning Efficient Graph Neural Networks зџґд ћ This paper tries to learn a small gnn (called tinygnn), which can achieve high performance and infer the node representation in a short time, and leverages peer node information to model the local structure explicitly and adopt a neighbor distillation strategy to learn local structure knowledge from a deeper gnn implicitly. recently, graph neural networks (gnns) arouse a lot of research. Doi: 10.1145 3394486.3403236 access: closed type: conference or workshop paper metadata version: 2021 03 09. (doi: 10.1145 3394486.3403236) recently, graph neural networks (gnns) arouse a lot of research interest and achieve great success in dealing with graph based data. the basic idea of gnns is to aggregate neighbor information iteratively. after k iterations, a k layer gnn can capture nodes' k hop local structure. in this way, a deeper gnn can access much more neighbor information leading to. Bencheng yan et al. 2020. tinygnn: learning efficient graph neural networks. in kdd '20.

и єж з и пјљkdd 20 Tinygnn Learning Efficient Graph Neural Networks зџґд ћ (doi: 10.1145 3394486.3403236) recently, graph neural networks (gnns) arouse a lot of research interest and achieve great success in dealing with graph based data. the basic idea of gnns is to aggregate neighbor information iteratively. after k iterations, a k layer gnn can capture nodes' k hop local structure. in this way, a deeper gnn can access much more neighbor information leading to. Bencheng yan et al. 2020. tinygnn: learning efficient graph neural networks. in kdd '20. Kdd'20 tinygnn: learning efficient graph neural networks [no code] arxiv’21 distilling self knowledge from contrastive links to classify graph nodes without passing messages ; miccai'21 gkd: semi supervised graph knowledge distillation for graph independent inference. Learning adaptive node embeddings across graphs. tinygnn: learning efficient graph neural networks. august 2020 kdd '20:.