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

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Showing 1376–1400 of 1570 papers

TitleStatusHype
Graph Learning via Spectral Densification—0
Incremental Learning on Growing Graphs—0
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance—0
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection—0
Online Discriminative Graph Learning from Multi-Class Smooth Signals—0
Inductive Collaborative Filtering via Relation Graph Learning—0
Bosonic Random Walk Networks for Graph Learning—0
Algorithms for Learning Graphs in Financial MarketsCode0
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks—0
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph LearningCode0
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric SpaceCode0
Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Adversarial Attacks on Deep Graph Matching—0
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization—0
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach—0
Node-Centric Graph Learning from Data for Brain State Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified