SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 876–900 of 1570 papers

TitleStatusHype
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?—0
Graph Learning Under Partial Observability—0
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?—0
Iterative Deep Graph Learning for Graph Neural Networks—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations—0
Iterative Graph Self-Distillation—0
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks—0
Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting—0
Joint Feature and Differentiable k -NN Graph Learning using Dirichlet Energy—0
Joint Graph and Vertex Importance Learning—0
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome—0
Joint Graph Estimation and Signal Restoration for Robust Federated Learning—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
Contrastive Graph Few-Shot Learning—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Topology-aware Tensor Decomposition for Meta-graph Learning—0
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach—0
Joint Signal Recovery and Graph Learning from Incomplete Time-Series—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified