Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Self-supervised Learning and Graph Classification under Heterophily
Self-supervised learning has shown its promising capability in graph representation learning in recent work. Most existing pre-training strategies usually choose the popular Graph neural networks (GNNs), which can be see…
ClassificationGraph ClassificationGraph Representation LearningMolecular Property Prediction+4CARL-G: Clustering-Accelerated Representation Learning on Graphs
Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of impediments, which prevent them from re…
ClusteringContrastive LearningGraph Representation LearningNode Classification+3Virtual Node Tuning for Few-shot Node Classification
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer s…
ClassificationGraph Representation LearningMeta-LearningNode Classification+1CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to …
Contrastive LearningDomain AdaptationGraph ClassificationGraph Representation Learning+1Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the sparse points/voxels as nodes and adopts gra…
Event data classificationGraph Representation LearningRepresentation LearningPANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growing interest in leveraging graph neural n…
DenoisingGraph Representation LearningRecommendation SystemsRepresentation LearningGraph-Level Embedding for Time-Evolving Graphs
Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level r…
Anomaly DetectionGraph Representation LearningGraph SimilarityLanguage Modeling+4Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
Representation learning on text-attributed graphs (TAGs) has become a critical research problem in recent years. A typical example of a TAG is a paper citation graph, where the text of each paper serves as node attribute…
Decision MakingGeneral KnowledgeGraph Neural NetworkGraph Representation Learning+5GIMM: InfoMin-Max for Automated Graph Contrastive Learning
Graph contrastive learning (GCL) shows great potential in unsupervised graph representation learning. Data augmentation plays a vital role in GCL, and its optimal choice heavily depends on the downstream task. Many GCL m…
Contrastive LearningData AugmentationGraph ClassificationGraph Representation Learning+1Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering
The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and…
ClusteringContrastive LearningGraph Representation LearningKnowledge Graph Completion+2Union Subgraph Neural Networks
Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) test as they operat…
Computational EfficiencyGraph Representation LearningRepresentation LearningCausal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention
Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent…
Graph Neural NetworkGraph Representation LearningRepresentation LearningTokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To this end, we propose a Neighborhood Aggrega…
Data AugmentationGraph Representation LearningNode ClassificationRepresentation LearningGraph Propagation Transformer for Graph Representation Learning
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the a…
Graph LearningGraph Property PredictionGraph RegressionGraph Representation Learning+2Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by ver…
Graph ClassificationGraph Representation LearningRepresentation LearningNeural Oscillators are Universal
Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physical neural networks that are used in analo…
Graph Representation LearningRepresentation LearningSemantic Random Walk for Graph Representation Learning in Attributed Graphs
In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the …
AttributeCommunity DetectionGraph Representation LearningNetwork Embedding+1Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Transforms. In this work, we develop a novel …
Computational EfficiencyGraph LearningGraph RegressionGraph Representation Learning+1Dynamic Graph Representation Learning for Depression Screening with Transformer
Early detection of mental disorder is crucial as it enables prompt intervention and treatment, which can greatly improve outcomes for individuals suffering from debilitating mental affliction. The recent proliferation of…
Contrastive LearningDepression DetectionFeature EngineeringGraph Representation Learning+1Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task. Existing methods based on…
DecoderGraph Representation LearningMachine Reading ComprehensionQuestion Answering+2