Description
An RGCN, or Relational Graph Convolution Network, is a an application of the GCN framework to modeling relational data, specifically to link prediction and entity classification tasks.
See here for an in-depth explanation of RGCNs by DGL.
Papers Using This Method
Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling2024-09-17Relational Graph Convolutional Networks for Sentiment Analysis2024-04-16GradXKG: A Universal Explain-per-use Temporal Knowledge Graph Explainer2023-10-07Message Intercommunication for Inductive Relation Reasoning2023-05-23Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions2023-02-08Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures2023-01-16MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark2023-01-03Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition2022-12-11Mitigating Relational Bias on Knowledge Graphs2022-11-26SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning2022-07-11ComDensE : Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion2022-06-29Ethereum Fraud Detection with Heterogeneous Graph Neural Networks2022-03-23Relational Graph Convolutional Networks: A Closer Look2021-07-21Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules2021-07-04Medical Entity Disambiguation Using Graph Neural Networks2021-04-03Detecting Hostile Posts using Relational Graph Convolutional Network2021-01-10Modeling Global Semantics for Question Answering over Knowledge Bases2021-01-05Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing2020-07-20Evaluating Logical Generalization in Graph Neural Networks2020-03-14How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos2018-12-02