Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP). Initi…
Combinatorial OptimizationGraph Neural NetworkGraph Representation Learningreinforcement-learning+4Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
Dynamic graphs (DG) are often used to describe evolving interactions between nodes in real-world applications. Temporal patterns are a natural feature of DGs and are also key to representation learning. However, existing…
Graph Representation LearningRepresentation LearningContrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a gra…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation LearningGraph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in th…
Graph Representation LearningRepresentation LearningVariational InferenceLeveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs (HGNNs) have advanced node embeddings and relationship learning for various tasks. However, existing methods often rely on domain-specific predef…
Graph Representation LearningLink PredictionRepresentation LearningUnveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biological neurons. Despite this, the applicatio…
Graph LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningHarvesting Textual and Structured Data from the HAL Publication Repository
HAL (\textit{Hyper Articles en Ligne}) is the French national publication repository, used by most higher education and research organizations for their open science policy. Although it is a rich repository of academic d…
ArticlesAuthorship AttributionContrastive LearningFill Mask+6Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs), has received increasing attention owing to its efficacy in handling graph-structured data. However, existing UGRL methods ideally a…
Graph Representation LearningRepresentation LearningSemantic Communication Enhanced by Knowledge Graph Representation Learning
This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic communications. The proposed approach leverages semantic and pragmat…
Graph Representation LearningKnowledge GraphsRepresentation LearningSemantic CommunicationScalable Graph Compressed Convolutions
Designing effective graph neural networks (GNNs) with message passing has two fundamental challenges, i.e., determining optimal message-passing pathways and designing local aggregators. Previous methods of designing opti…
Graph Representation LearningRepresentation LearningDTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry pra…
Graph Representation LearningRepresentation LearningYour Graph Recommender is Provably a Single-view Graph Contrastive Learning
Graph recommender (GR) is a type of graph neural network (GNNs) encoder that is customized for extracting information from the user-item interaction graph. Due to its strong performance on the recommendation task, GR has…
Contrastive LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningExploring the Role of Node Diversity in Directed Graph Representation Learning
Manymethods of Directed Graph Neural Networks (DGNNs) are designed to equally treat nodes in the same neighbor set (i.e., out-neighbor set and in-neighbor set) for every node, without consider ing the node diversi…
DiversityGraph Representation LearningLink PredictionRepresentation LearningPolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer
Spectral Graph Neural Networks have demonstrated superior performance in graph representation learning. However, many current methods focus on employing shared polynomial coefficients for all nodes, i.e., learning node-u…
Graph Representation LearningRepresentation LearningHHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning
Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in modeling real-world Heterogeneous Information Networks (HINs), challenges such as expressiveness limitations and over-smoothing have prompted research…
Graph Representation LearningNode ClusteringRepresentation LearningThe Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networks (GNNs) over tradi…
Graph LearningGraph Representation LearningRepresentation LearningUnsupervised Graph Representation Learning with Inductive Shallow Node Embedding
Network science has witnessed a surge in popularity, driven by the transformative power of node representation learning for diverse applications like social network analysis and biological modeling. While shallow embeddi…
Graph Representation LearningInductive LearningNode ClassificationRepresentation LearningEdge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, …
Edge-computingGraph LearningGraph Representation LearningMiscellaneous+1Differential Encoding for Improved Representation Learning over Graphs
Combining the message-passing paradigm with the global attention mechanism has emerged as an effective framework for learning over graphs. The message-passing paradigm and the global attention mechanism fundamentally gen…
Graph Representation LearningRepresentation LearningHeterogeneous Graph Contrastive Learning with Spectral Augmentation
Heterogeneous graphs can well describe the complex entity relationships in the real world. For example, online shopping networks contain multiple physical types of consumers and products, as well as multiple relationship…
Contrastive LearningData AugmentationGraph Representation LearningRepresentation Learning