Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot ca…
ClusteringGraph Representation LearningRepresentation LearningSynergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN
The fast development of location-based social networks (LBSNs) has led to significant changes in society, resulting in popular studies of using LBSN data for socioeconomic prediction, e.g., regional population and commer…
Graph Representation LearningPredictionRepresentation LearningSparse Decomposition of Graph Neural Networks
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of …
Graph Neural NetworkGraph Representation LearningNode ClassificationRepresentation Learning+1Theoretical Insights into Line Graph Transformation on Graph Learning
Line graph transformation has been widely studied in graph theory, where each node in a line graph corresponds to an edge in the original graph. This has inspired a series of graph neural networks (GNNs) applied to trans…
Graph LearningGraph Representation LearningRepresentation LearningExplanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
Graph representation learning (GRL), enhanced by graph augmentation methods, has emerged as an effective technique achieving performance improvements in wide tasks such as node classification and graph classification. In…
Graph ClassificationGraph Representation LearningNode ClassificationRepresentation LearningTowards Fair Graph Representation Learning in Social Networks
With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim to ensure that node representations can b…
Data AugmentationFairnessGraph Representation LearningRepresentation LearningBridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning
Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing …
Graph Representation LearningGraph structure learningRepresentation LearningSmall Language ModelQuerying functional and structural niches on spatial transcriptomics data
Cells in multicellular organisms coordinate to form functional and structural niches. With spatial transcriptomics enabling gene expression profiling in spatial contexts, it has been revealed that spatial niches serve as…
Contrastive LearningGraph Representation LearningPrognosisRepresentation LearningInformation propagation dynamics in Deep Graph Networks
Graphs are a highly expressive abstraction for modeling entities and their relations, such as molecular structures, social networks, and traffic networks. Deep Graph Networks (DGNs) have emerged as a family of deep learn…
Graph Representation LearningRepresentation LearningA Benchmark on Directed Graph Representation Learning in Hardware Designs
To keep pace with the rapid advancements in design complexity within modern computing systems, directed graph representation learning (DGRL) has become crucial, particularly for encoding circuit netlists, computational g…
Graph Representation LearningRepresentation LearningDiffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. Usually, a comprehensive hyperparameter t…
Graph Representation LearningLink PredictionNode ClassificationRepresentation LearningHaste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications with large-scale graphs. A class of pro…
Graph Representation LearningRepresentation LearningDiss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
The Euler Characteristic Transform (ECT) is an efficiently-computable geometrical-topological invariant that characterizes the global shape of data. In this paper, we introduce the Local Euler Characteristic Transform ($…
Graph Representation LearningNode ClassificationRepresentation LearningClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations
Graph Neural Networks (GNNs) have revolutionized the domain of graph representation learning by utilizing neighborhood aggregation schemes in many popular architectures, such as message passing graph neural networks (MPG…
Graph Representation LearningLink PredictionNode ClassificationRepresentation LearningPROXI: Challenging the GNNs for Link Prediction
Over the past decade, Graph Neural Networks (GNNs) have transformed graph representation learning. In the widely adopted message-passing GNN framework, nodes refine their representations by aggregating information from n…
AttributeGraph Representation LearningLink PredictionPrediction+1TopER: Topological Embeddings in Graph Representation Learning
Graph embeddings play a critical role in graph representation learning, allowing machine learning models to explore and interpret graph-structured data. However, existing methods often rely on opaque, high-dimensional em…
ClusteringGraph ClusteringGraph Representation LearningRepresentation Learning+1Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
Representation learning on text-attributed graphs (TAGs) has attracted significant interest due to its wide-ranging real-world applications, particularly through Graph Neural Networks (GNNs). Traditional GNN methods focu…
Graph Representation LearningRepresentation LearningWhole-Graph Representation Learning For the Classification of Signed Networks
Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional representations of graphs, ha…
Graph EmbeddingGraph Representation LearningRepresentation LearningNeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes
Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional…
2-task Classification4-task ClassificationAlzheimer's DetectionFunctional Connectivity+4Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition
Context-aware Human Activity Recognition (CHAR) is challenging due to the need to recognize the user's current activity from signals that vary significantly with contextual factors such as phone placements and the varied…
Activity RecognitionGraph Representation LearningHuman Activity RecognitionRepresentation Learning