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 551–600 of 982 papers

TitleStatusHype
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
A Survey on Malware Detection with Graph Representation Learning—0
A Survey on Spectral Graph Neural Networks—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Asymmetric Graph Representation Learning—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
BCDR: Betweenness Centrality-based Distance Resampling for Graph Shortest Distance Embedding—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
Uplifting Message Passing Neural Network with Graph Original Information—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention—0
Causal Machine Learning: A Survey and Open Problems—0
CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning—0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
CN-Motifs Perceptive Graph Neural Networks—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Convexified Message-Passing Graph Neural Networks—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
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Benchmark Results

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