SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 826–850 of 982 papers

TitleStatusHype
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Graphine: A Dataset for Graph-aware Terminology Definition GenerationCode0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?Code0
LASE: Learned Adjacency Spectral EmbeddingsCode0
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient MatchingCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Graph Convolutional Networks with EigenPoolingCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Graph Contrastive Learning for Connectome ClassificationCode0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Graph Communal Contrastive LearningCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Variational Graph Contrastive LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified