Graph Learning
223 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Graph Learning
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find Graph Learning models and implementations
BotNet
Mindboggle
ETHZ-Shape
Most implemented papers
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
Grand20/grand
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We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
THUDM/GCC
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Graph representation learning has emerged as a powerful technique for addressing real-world problems.
A Unified Framework for Structured Graph Learning via Spectral Constraints
Then we develop an optimization framework that leverages graph learning with specific structures via spectral constraints on graph matrices.
Graph Construction from Data using Non Negative Kernel regression (NNK Graphs)
STAC-USC/PyNNK_graph_construction
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Data driven graph constructions are often used in various applications, including several machine learning tasks, where the goal is to make predictions and discover patterns.
A Fair Comparison of Graph Neural Networks for Graph Classification
diningphil/gnn-comparison
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ICLR 2020
We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
DeeperGCN: All You Need to Train Deeper GCNs
dmlc/dgl
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Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
Learning on Attribute-Missing Graphs
xuChenSJTU/SAT-master-online
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Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Bilinear Scoring Function Search for Knowledge Graph Learning
We first set up a search space for AutoBLM by analyzing existing scoring functions.
