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
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Normed Spaces for Graph EmbeddingCode0
Heterogeneous Deep Graph InfomaxCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
Gossip and Attend: Context-Sensitive Graph Representation LearningCode0
OLGA: One-cLass Graph AutoencoderCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain GraphCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
A Deep Latent Space Model for Graph Representation LearningCode0
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?Code0
On the Initialization of Graph Neural NetworksCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Towards Real-Time Temporal Graph LearningCode0
Open Domain Question Answering Using Early Fusion of Knowledge Bases and TextCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
MGC: A Complex-Valued Graph Convolutional Network for Directed GraphsCode0
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time SpansCode0
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
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
Wasserstein Hypergraph Neural Network—0
XLVIN: eXecuted Latent Value Iteration Nets—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
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Benchmark Results

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