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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 of 982 papers

TitleStatusHype
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
Temporal knowledge graph representation learning with local and global evolutionsCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative SamplingCode0
Do Transformers Really Perform Badly for Graph Representation?Code0
A Deep Latent Space Model for Graph Representation LearningCode0
Topological Pooling on GraphsCode0
Towards Expressive Graph RepresentationCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Heterogeneous Deep Graph InfomaxCode0
Towards Graph Representation Learning Based Surgical Workflow AnticipationCode0
Towards Improved Illicit Node Detection with Positive-Unlabelled LearningCode0
Towards Real-Time Temporal Graph LearningCode0
Transformers are efficient hierarchical chemical graph learnersCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient MatchingCode0
Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
Understanding microbiome dynamics via interpretable graph representation learningCode0
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