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 451–475 of 982 papers

TitleStatusHype
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
Domain Adaptive Graph Classification—0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time SpansCode0
LightGCN: Evaluated and EnhancedCode0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
Dynamic Spiking Framework for Graph Neural Networks—0
Symmetry Breaking and Equivariant Neural Networks—0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
Understanding Community Bias Amplification in Graph Representation Learning—0
On the Initialization of Graph Neural NetworksCode0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
Normed Spaces for Graph EmbeddingCode0
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes—0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Show:102550
← PrevPage 19 of 40Next →

Benchmark Results

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