Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most …
Graph Representation LearningRepresentation LearningGQWformer: A Quantum-based Transformer for Graph Representation Learning
Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism in GTs tends to neglect the inductive bi…
Graph ClassificationGraph Representation LearningInductive BiasRepresentation LearningFrom ChebNet to ChebGibbsNet
Recent advancements in Spectral Graph Convolutional Networks (SpecGCNs) have led to state-of-the-art performance in various graph representation learning tasks. To exploit the potential of SpecGCNs, we analyze correspond…
Graph Representation LearningNode ClassificationRepresentation LearningToward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predictions caused by sensitive attributes, su…
FairnessGraph Representation LearningKnowledge DistillationRepresentation LearningPerturbation Ontology based Graph Attention Networks
In recent years, graph representation learning has undergone a paradigm shift, driven by the emergence and proliferation of graph neural networks (GNNs) and their heterogeneous counterparts. Heterogeneous GNNs have shown…
Graph AttentionGraph Representation LearningLink PredictionNode Classification+2GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
Graph Transformers (GTs) have demonstrated remarkable performance in incorporating various graph structure information, e.g., long-range structural dependency, into graph representation learning. However, self-attention …
Graph ClassificationGraph Representation LearningNode ClassificationRepresentation LearningInstance-Aware Graph Prompt Learning
Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availability of high-quality labels in an end-t…
Graph Representation LearningPrompt LearningRepresentation LearningTANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
In the field of deep learning, Graph Neural Networks (GNNs) and Graph Transformer models, with their outstanding performance and flexible architectural designs, have become leading technologies for processing structured …
Computational EfficiencyGraph Neural NetworkGraph RegressionGraph Representation Learning+3Conditional Distribution Learning on Graphs
Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural networ…
Contrastive LearningData AugmentationGraph ClassificationGraph Neural Network+2A survey on Graph Deep Representation Learning for Facial Expression Recognition
This comprehensive review delves deeply into the various methodologies applied to facial expression recognition (FER) through the lens of graph representation learning (GRL). Initially, we introduce the task of FER and t…
Facial Expression RecognitionFacial Expression Recognition (FER)Graph Representation LearningRepresentation LearningVariational Graph Contrastive Learning
Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised learning (SSL) methods are widely used …
Contrastive LearningGraph Representation LearningRepresentation LearningSelf-Supervised LearningAn Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
Incremental graph learning has gained significant attention for its ability to address the catastrophic forgetting problem in graph representation learning. However, traditional methods often rely on a large number of la…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningGraph Learning+6HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for derivin…
Computational EfficiencyGraph Representation LearningLink PredictionNode Classification+1Shedding Light on Problems with Hyperbolic Graph Learning
Recent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node class…
Graph LearningGraph Representation LearningLink PredictionNode Classification+1Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning
Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present a…
Graph Representation LearningKnowledge GraphsRepresentation LearningPost-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns about their real-world applicability. Wh…
Graph Representation LearningNode ClassificationRelational ReasoningRepresentation LearningNon-Euclidean Mixture Model for Social Network Embedding
It is largely agreed that social network links are formed due to either homophily or social influence. Inspired by this, we aim at understanding the generation of links via providing a novel embedding-based graph formati…
DecoderGraph Representation LearningmodelNetwork Embedding+1Centrality Graph Shift Operators for Graph Neural Networks
Graph Shift Operators (GSOs), such as the adjacency and graph Laplacian matrices, play a fundamental role in graph theory and graph representation learning. Traditional GSOs are typically constructed by normalizing the a…
Graph AttentionGraph Neural NetworkGraph Representation LearningRepresentation LearningQuery-Efficient Adversarial Attack Against Vertical Federated Graph Learning
Graph neural network (GNN) has captured wide attention due to its capability of graph representation learning for graph-structured data. However, the distributed data silos limit the performance of GNN. Vertical federate…
Adversarial AttackFederated LearningGraph LearningGraph Neural Network+3Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a dominant approach in graph representation learning, yet they often struggle to capture consistent similarity relationships among graphs. While graph kernel methods such as t…
Graph ClassificationGraph Representation LearningRepresentation Learning