Edge Classification
27 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Edge Classification
DTGB
Most implemented papers
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
IBM/EvolveGCN
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Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Global Self-Attention as a Replacement for Graph Convolution
shamim-hussain/egt_pytorch
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The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data.
DiGress: Discrete Denoising diffusion for graph generation
cvignac/digress
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This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
mobarakol/Surgical_SceneGraph_Generation
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Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.
Adaptive Edge Attention for Graph Matching with Outliers
bestwei/eagm
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International Joint Conference on Artificial Intelligence 2021
To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs.
Graph Neural Network for Cell Tracking in Microscopy Videos
By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for the maximal paths in the graph.
Retrieval Augmented Generation using Engineering Design Knowledge
siddharthl93/engineering-design-knowledge
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For this task, we create a dataset of 375, 084 examples and fine-tune language models for relation identification (token classification) and elicitation (sequence-to-sequence).
Dynamic Graph Convolutional Networks Using the Tensor M-Product
IBM/TM-GCN
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ICLR 2020
In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
