Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
Temporal graph representation learning (TGRL) is essential for modeling dynamic systems in real-world networks. However, traditional TGRL methods, despite their effectiveness, often face significant computational challen…
GPUGraph Representation LearningLink PredictionNode Classification+1L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are o…
DecoderGraph Representation LearningNetwork EmbeddingNode Classification+1A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements. Earlier techniques often operate in an end-to-end setting, which heavily rely on the availability of ample labeled…
Few-Shot LearningGraph Representation LearningMeta-LearningPrompt Learning+2Graph Domain Adaptation: Challenges, Progress and Prospects
As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective knowledge-transfer paradigm across graphs. I…
Domain AdaptationGRAPH DOMAIN ADAPTATIONGraph Representation LearningRepresentation Learning+2Graph Transformers without Positional Encodings
Recently, Transformers for graph representation learning have become increasingly popular, achieving state-of-the-art performance on a wide-variety of graph datasets, either alone or in combination with message-passing g…
Graph ClassificationGraph RegressionGraph Representation LearningNode Classification+1Graph Contrastive Learning with Cohesive Subgraph Awareness
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation,…
Contrastive LearningGraph ClassificationGraph LearningGraph Representation Learning+1GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown…
Adversarial RobustnessContrastive LearningGraph ClassificationGraph Representation Learning+2Product Manifold Representations for Learning on Biological Pathways
Machine learning models that embed graphs in non-Euclidean spaces have shown substantial benefits in a variety of contexts, but their application has not been studied extensively in the biological domain, particularly wi…
Graph Neural NetworkGraph Representation LearningRepresentation LearningEMP: Effective Multidimensional Persistence for Graph Representation Learning
Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH)…
Graph ClassificationGraph Representation LearningRepresentation LearningTopological Data AnalysisGradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation lea…
Entity AlignmentEntity EmbeddingsGraph Representation LearningKnowledge Graphs+3Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained architectural layout generation tasks. The pr…
Generative Adversarial NetworkGraph Representation LearningLayout GenerationNode Classification+1Graph Representation Learning for Contention and Interference Management in Wireless Networks
Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group's transmissions. We will find the optimal user grouping de…
graph constructionGraph Representation LearningManagementRepresentation LearningTensor Graph Convolutional Network for Dynamic Graph Representation Learning
Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model…
Graph Representation LearningRepresentation LearningAccurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while…
Graph ClassificationGraph Representation LearningOut-of-Distribution DetectionRepresentation LearningMotif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
Graphs are typical non-Euclidean data of complex structures. In recent years, Riemannian graph representation learning has emerged as an exciting alternative to Euclidean ones. However, Riemannian methods are still in an…
Contrastive LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningAdversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
With the release of increasing open-source emotion recognition datasets on social media platforms and the rapid development of computing resources, multimodal emotion recognition tasks (MER) have begun to receive widespr…
Contrastive LearningEmotion RecognitionGraph Representation LearningMultimodal Emotion Recognition+1DiffKG: Knowledge Graph Diffusion Model for Recommendation
Knowledge Graphs (KGs) have emerged as invaluable resources for enriching recommendation systems by providing a wealth of factual information and capturing semantic relationships among items. Leveraging KGs can significa…
Data AugmentationGraph Representation LearningKnowledge Graphsmodel+2PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering
Recently, many carefully crafted graph representation learning methods have achieved impressive performance on either strong heterophilic or homophilic graphs, but not both. Therefore, they are incapable of generalizing …
Graph Representation LearningNode ClassificationRepresentation LearningPUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation
When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is easily overwritten when learning with newly…
Continual LearningGraph LearningGraph Representation LearningNode Classification+1Domain Adaptive Graph Classification
Despite the remarkable accomplishments of graph neural networks (GNNs), they typically rely on task-specific labels, posing potential challenges in terms of their acquisition. Existing work have been made to address this…
ClassificationDomain AdaptationGraph ClassificationGraph Representation Learning+2