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 676–700 of 982 papers

TitleStatusHype
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
Fundamental Limits of Deep Graph Convolutional Networks—0
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version—0
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
TopER: Topological Embeddings in Graph Representation Learning—0
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation—0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Towards Fair Graph Representation Learning in Social Networks—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Towards Graph Representation Learning in Emergent Communication—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
Towards Interpretable Molecular Graph Representation Learning—0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
Transferable Graph Backdoor Attack—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning—0
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning—0
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Benchmark Results

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