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 576–600 of 982 papers

TitleStatusHype
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