Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time Spans
Social recommendations have been widely adopted in substantial domains. Recently, graph neural networks (GNN) have been employed in recommender systems due to their success in graph representation learning. However, deal…
Graph Representation LearningRecommendation SystemsRepresentation LearningHierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
Graph contrastive learning (GCL) aims to align the positive features while differentiating the negative features in the latent space by minimizing a pair-wise contrastive loss. As the embodiment of an outstanding discrim…
Contrastive LearningGraph Representation LearningRepresentation LearningTransfer LearningGraph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking in generalization capability when tackling out-of-distribution (OOD) data. Graph invariant learning…
Graph Representation LearningOut-of-Distribution GeneralizationRepresentation LearningLightGCN: Evaluated and Enhanced
This paper analyses LightGCN in the context of graph recommendation algorithms. Despite the initial design of Graph Convolutional Networks for graph classification, the non-linear operations are not always essential. Lig…
Graph ClassificationGraph Representation LearningRecommendation SystemsscBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
Single-cell RNA sequencing (scRNA-seq) technology provides high-throughput gene expression data to study the cellular heterogeneity and dynamics of complex organisms. Graph neural networks (GNNs) have been widely used fo…
ClassificationGraph Representation LearningRepresentation LearningDynamic Spiking Framework for Graph Neural Networks
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented b…
Dynamic Node ClassificationGraph Representation LearningNode ClassificationRepresentation LearningSymmetry Breaking and Equivariant Neural Networks
Using symmetry as an inductive bias in deep learning has been proven to be a principled approach for sample-efficient model design. However, the relationship between symmetry and the imperative for equivariance in neural…
Combinatorial OptimizationGraph Representation LearningInductive BiasRepresentation LearningEdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning
Graph Contrastive Learning (GCL) is unsupervised graph representation learning that can obtain useful representation of unknown nodes. The node representation can be utilized as features of downstream tasks. However, GCL…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation LearningIsomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is desirable to develop t…
Graph ClassificationGraph LearningGraph Neural NetworkGraph Representation Learning+3Understanding Community Bias Amplification in Graph Representation Learning
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different classes by graph representation learning. We…
Contrastive LearningData AugmentationGraph Representation LearningRepresentation LearningRelational Deep Learning: Graph Representation Learning on Relational Databases
Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations. However, building machine learning mode…
Deep LearningFeature EngineeringGraph Representation LearningRepresentation LearningOn the Initialization of Graph Neural Networks
Graph Neural Networks (GNNs) have displayed considerable promise in graph representation learning across various applications. The core learning process requires the initialization of model weight matrices within each GN…
Graph ClassificationGraph Representation LearningLink PredictionNode Classification+1HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised fra…
Graph Representation LearningPrompt LearningRepresentation LearningLarge-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Graph Transformers have recently been successful in various graph representation learning tasks, providing a number of advantages over message-passing Graph Neural Networks. Utilizing Graph Transformers for learning the …
Functional ConnectivityGender ClassificationGraph Representation LearningRepresentation LearningRecurrent Distance Filtering for Graph Representation Learning
Graph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing the information from distant nodes effectively. Conversely, graph transformers allow each node to attend to all o…
Graph ClassificationGraph Representation LearningInductive BiasLong-range modeling+2Normed Spaces for Graph Embedding
Theoretical results from discrete geometry suggest that normed spaces can abstractly embed finite metric spaces with surprisingly low theoretical bounds on distortion in low dimensions. In this paper, inspired by this th…
Graph EmbeddingGraph ReconstructionGraph Representation LearningLink Prediction+2HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
Many graph representation learning (GRL) problems are dynamic, with millions of edges added or removed per second. A fundamental workload in this setting is dynamic link prediction: using a history of graph updates to pr…
Dynamic Link PredictionGraph Representation LearningLink PredictionPrediction+1GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs
Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support training on graph streams. In contrast, many re…
CPUGPUGraph LearningGraph Representation Learning+2MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Graphs can inherently model interconnected objects on the Web, thereby facilitating a series of Web applications, such as web analyzing and content recommendation. Recently, Graph Neural Networks (GNNs) have emerged as a…
General KnowledgeGraph Representation LearningRepresentation LearningGeneralized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision. To reduce labeling requirement, the "pre-train, prompt"…
Graph Representation LearningLink PredictionRepresentation Learning