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 551–575 of 1570 papers

TitleStatusHype
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation—0
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
Accurately Solving Physical Systems with Graph Learning—0
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization—0
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm—0
Do graph neural network states contain graph properties?—0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems—0
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification—0
Distribution Preserving Graph Representation Learning—0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
Distributed Graph Learning with Smooth Data Priors—0
Each Graph is a New Language: Graph Learning with LLMs—0
Distilling Large Language Models for Text-Attributed Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified