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 1101–1125 of 1570 papers

TitleStatusHype
When Does A Spectral Graph Neural Network Fail in Node Classification?—0
When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty—0
Deep Semantic Graph Learning via LLM based Node Enhancement—0
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
An Uncoupled Training Architecture for Large Graph Learning—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
Demystifying Graph Convolution with a Simple Concatenation—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
Understanding Multistationarity of Fully Open Reaction Networks—0
Detecting Low Pass Graph Signals via Spectral Pattern: Sampling Complexity and Applications—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment—0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
A General Benchmark Framework is Dynamic Graph Neural Network Need—0
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption—0
A Generative Graph Method to Solve the Travelling Salesman Problem—0
Diffusion Maps for Signal Filtering in Graph Learning—0
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World—0
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation—0
Directional diffusion models for graph representation learning—0
Dirichlet Active Learning—0
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs—0
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified