Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference
Accurate acquisition of crowd flow at Points of Interest (POIs) is pivotal for effective traffic management, public service, and urban planning. Despite this importance, due to the limitations of urban sensing techniques…
Contrastive LearningGraph Representation LearningRepresentation LearningSelf-Learning+1Graph Self-Contrast Representation Learning
Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representation learning. Some existing methods adopt the 1-vs-K scheme to construct one positive and K negative samples for each gra…
Contrastive LearningGraph Representation LearningRepresentation LearningTransfer Learning+1RDGSL: Dynamic Graph Representation Learning with Structure Learning
Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can …
Graph Representation LearningGraph structure learningRepresentation LearningPure Message Passing Can Estimate Common Neighbor for Link Prediction
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle, surpassed by simple heuristics such as…
Graph Representation LearningLink PredictionPredictionRepresentation LearningRethinking the Power of Graph Canonization in Graph Representation Learning with Stability
The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomo…
Graph Representation LearningRepresentation LearningConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling
Contrastive learning has made breakthrough advancements in graph representation learning, which encourages the representation of positive samples to be close and those of negative samples to be far away. However, existin…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation Learning+1Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e…
Contrastive LearningGraph Representation LearningRepresentation LearningCompanion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. However, learning representations to capture …
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingLink Prediction+2A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification. In this paper, we prop…
Graph Representation LearningLink PredictionNetwork EmbeddingNode Classification+1RESTORE: Graph Embedding Assessment Through Reconstruction
Following the success of Word2Vec embeddings, graph embeddings (GEs) have gained substantial traction. GEs are commonly generated and evaluated extrinsically on downstream applications, but intrinsic evaluations of the o…
Graph EmbeddingGraph ReconstructionGraph Representation LearningRepresentation LearningTransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-based recommender systems involves recur…
Collaborative FilteringGraph ClassificationGraph Representation LearningRecommendation Systems+1Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning
Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting au…
Graph Representation LearningRepresentation LearningSemantic Graph Representation Learning for Handwritten Mathematical Expression Recognition
Handwritten mathematical expression recognition (HMER) has attracted extensive attention recently. However, current methods cannot explicitly study the interactions between different symbols, which may fail when faced si…
Graph Representation LearningRepresentation LearningThe Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of comp…
Graph Representation LearningRepresentation LearningOCTAL: Graph Representation Learning for LTL Model Checking
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, suffer from the state space explosion problem due to cross …
Binary ClassificationGraph Representation LearningRepresentation LearningTransitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link predi…
Graph ClassificationGraph Representation LearningIsomorphism TestingLink Prediction+4Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks
Representation learning methods have revolutionized machine learning on networks by converting discrete network structures into continuous domains. However, dynamic networks that evolve over time pose new challenges. To …
Graph Representation LearningLink PredictionRepresentation LearningBiomedical Knowledge Graph Embeddings with Negative Statements
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingl…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graph Embeddings+5Local Structure-aware Graph Contrastive Representation Learning
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackle the label problem effectively, mainl…
Contrastive LearningGraph Neural NetworkGraph Representation LearningLink Prediction+2Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Altho…
ClassificationGraph LearningGraph Representation LearningPatent classification+1