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 101–125 of 982 papers

TitleStatusHype
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Sparse Decomposition of Graph Neural Networks—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
Towards Fair Graph Representation Learning in Social Networks—0
Information propagation dynamics in Deep Graph Networks—0
Querying functional and structural niches on spatial transcriptomics dataCode0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
PROXI: Challenging the GNNs for Link PredictionCode0
TopER: Topological Embeddings in Graph Representation Learning—0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Multi-object event graph representation learning for Video Question Answering—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
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Benchmark Results

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