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 601–625 of 1570 papers

TitleStatusHype
Understanding Multistationarity of Fully Open Reaction Networks—0
Automated Knowledge Graph Learning in Industrial Processes—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Demystifying Graph Convolution with a Simple Concatenation—0
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning—0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
Graph Learning for Cognitive Digital Twins in Manufacturing Systems—0
Deep Semantic Graph Learning via LLM based Node Enhancement—0
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks—0
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
Graph Learning for Inverse Landscape Genetics—0
Graph Learning for Planning: The Story Thus Far and Open Challenges—0
Graph learning methods to extract empathy supporting regions in a naturalistic stimuli fMRI—0
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning—0
DEEP GRAPH TREE NETWORKS—0
DEEP GRAPH SPECTRAL EVOLUTION NETWORKS FOR GRAPH TOPOLOGICAL TRANSFORMATION—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified