Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning
Attribute graphs are ubiquitous in multimedia applications, and graph representation learning (GRL) has been successful in analyzing attribute graph data. However, incomplete graph data and missing node attributes can ha…
AttributeContrastive LearningGraph Representation LearningImputation+2Hierarchical Transformer for Scalable Graph Learning
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning. However, as current implementations of…
Graph LearningGraph Representation LearningRepresentation LearningStrengthening structural baselines for graph classification using Local Topological Profile
We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. Our study focuses on model evaluation in the context of the …
Graph ClassificationGraph Representation LearningModel SelectionRepresentation LearningDeep Graph Representation Learning and Optimization for Influence Maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progress in designing various tradition…
Graph Representation LearningRepresentation LearningNeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-i…
Graph Representation LearningKnowledge GraphsRepresentation LearningConnector 0.5: A unified framework for graph representation learning
Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link …
Graph EmbeddingGraph GenerationGraph Representation LearningKnowledge Graphs+3Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Graph contrastive learning defines a contrastive task to pull similar instances close and push dissimilar instances away. It learns discriminative node embeddings without supervised labels, which has aroused increasing a…
Contrastive Learninggraph constructionGraph Representation LearningRepresentation LearningWhat Do GNNs Actually Learn? Towards Understanding their Representations
In recent years, graph neural networks (GNNs) have achieved great success in the field of graph representation learning. Although prior work has shed light on the expressiveness of those models (\ie whether they can dist…
Graph Representation LearningRepresentation LearningDynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer
Dynamic graph representation learning has emerged as a crucial research area, driven by the growing need for analyzing time-evolving graph data in real-world applications. While recent approaches leveraging recurrent neu…
Dynamic Link PredictionGraph Representation LearningLink PredictionRepresentation LearningStochastic Subgraph Neighborhood Pooling for Subgraph Classification
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting …
ClassificationData AugmentationGraph ClassificationGraph Representation Learning+1Multi-View Graph Representation Learning Beyond Homophily
Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning meth…
AttributeGraph Representation LearningRepresentation LearningSelf-Supervised LearningAccurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications. While deep learning methods learn protein contexts to establish feasib…
Graph Representation LearningRepresentation LearningDynamic Graph Representation Learning with Neural Networks: A Survey
In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynami…
Graph LearningGraph Neural NetworkGraph Representation LearningRecommendation Systems+2A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, in…
Graph EmbeddingGraph Neural NetworkGraph Representation LearningRepresentation Learning+1Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., l…
Graph Representation LearningNode ClassificationRepresentation LearningCAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs
Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this contex…
FairnessGraph LearningGraph Representation LearningLink Prediction+2Class-Imbalanced Learning on Graphs: A Survey
The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. T…
Graph Representation LearningRepresentation LearningSurveyGraph Representation Learning for Interactive Biomolecule Systems
Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of…
Deep LearningDrug DiscoveryGraph Representation LearningRepresentation LearningAttribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing wo…
AttributeEntity AlignmentGraph Neural NetworkGraph Representation Learning+4FMGNN: Fused Manifold Graph Neural Network
Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in the Euclidean space, while recent works extend the embedding models to …
Graph Neural NetworkGraph Representation LearningLink PredictionNode Classification+1