Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
HeGAE-AC: heterogeneous graph auto-encoder for attribute completion
Heterogeneous Graphs (HGs) are widely used to model complex real-world networks that involve multiple types of nodes and relations. Many heterogeneous graph neural networks (HGNNs) based methods have been developed to le…
AttributeGraph Representation LearningRepresentation LearningMulti-hop Attention-based Graph Pooling: A Personalized PageRank Perspective
Over the past ten years, graph representation learning has garnered a lot of attention due to the variety of graph-structured data and its efficiency in both time and space. One essential method for obtaining effective g…
Graph ClassificationGraph Representation LearningRepresentation LearningDecoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
Recent years have witnessed the great success of graph pre-training for graph representation learning. With hundreds of graph pre-training tasks proposed, integrating knowledge acquired from multiple pre-training tasks h…
Graph Representation LearningRepresentation LearningA Survey on Temporal Knowledge Graph: Representation Learning and Applications
Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with …
Graph Representation LearningKnowledge GraphsRepresentation LearningNegative Sampling in Knowledge Graph Representation Learning: A Review
Knowledge Graph Representation Learning (KGRL), or Knowledge Graph Embedding (KGE), is essential for AI applications such as knowledge construction and information retrieval. These models encode entities and relations in…
Graph EmbeddingGraph Representation LearningInformation RetrievalKnowledge Graph Embedding+5Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (…
Graph Representation LearningRepresentation LearningLocalGCL: Local-aware Contrastive Learning for Graphs
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-consuming and costly process of annotating g…
Contrastive LearningGraph Representation LearningRepresentation LearningSelf-Supervised LearningRepresentation learning in multiplex graphs: Where and how to fuse information?
In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community. However, most proposed methods are focused on homogeneous networks, whereas real-world grap…
Graph Representation LearningRepresentation LearningHyperdimensional Representation Learning for Node Classification and Link Prediction
We introduce Hyperdimensional Graph Learner (HDGL), a novel method for node classification and link prediction in graphs. HDGL maps node features into a very high-dimensional space (\textit{hyperdimensional} or HD space …
Classificationclass-incremental learningClass Incremental LearningGraph Learning+8Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is in…
Contrastive LearningGraph Representation LearningRepresentation LearningUnderstanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning
As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have bec…
Graph Representation LearningRepresentation LearningSurveyPosition: Topological Deep Learning is the New Frontier for Relational Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may co…
Deep LearningGraph Representation LearningPositionRelational Reasoning+1Graph Mamba: Towards Learning on Graphs with State Space Models
Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information over the graph by stacking multiple lay…
Graph Representation LearningMambaRepresentation LearningState Space ModelsExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning
Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded signals in unlabeled data. While SSL has …
Data AugmentationGraph GenerationGraph Representation LearningNode Classification+3TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation. Despite the success of TGNNs, the…
DenoisingFraud DetectionGPUGraph Representation Learning+1Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising directions, graph condensation methods …
Graph Representation LearningRepresentation LearningOn provable privacy vulnerabilities of graph representations
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper …
Graph Representation LearningRepresentation LearningEXGC: Bridging Efficiency and Explainability in Graph Condensation
Graph representation learning on vast datasets, like web data, has made significant strides. However, the associated computational and storage overheads raise concerns. In sight of this, Graph condensation (GCond) has be…
Graph Representation LearningRepresentation LearningA Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge in graph modeling. Despite recent graph…
DecoderGraph ClassificationGraph GenerationGraph Representation Learning+1Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harnesses the sophisticated linguistic capab…
Graph Representation LearningRepresentation Learning