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 476–500 of 982 papers

TitleStatusHype
Cell Attention NetworksCode0
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
Union Subgraph Neural NetworksCode0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
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
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
Urban Region Profiling via A Multi-Graph Representation Learning Framework—0
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft—0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
Virtual Node Tuning for Few-shot Node Classification—0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
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Benchmark Results

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