Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
Graph representation learning methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships. However, current approaches face three key challenges: (…
Graph Representation LearningCooperative Sheaf Neural Networks
Sheaf diffusion has recently emerged as a promising design pattern for graph representation learning due to its inherent ability to handle heterophilic data and avoid oversmoothing. Meanwhile, cooperative message passing…
Graph Representation LearningPermutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
Dynamic graphs exhibit complex temporal dynamics due to the interplay between evolving node features and changing network structures. Recently, Graph Neural Controlled Differential Equations (Graph Neural CDEs) successfu…
Graph Representation LearningRepresentation LearningHeterogeneous Temporal Hypergraph Neural Network
Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex networks, GRL methods designed for complex hete…
Contrastive LearningGraph Representation LearningDevil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Graph neural networks (GNNs) have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as us…
Data PoisoningGraph LearningGraph Representation LearningNode Classification+2Wasserstein Hypergraph Neural Network
The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learning has become mainstream over the past …
Graph Representation LearningNode ClassificationRepresentation LearningLanguage Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning
Neural Architecture Representation Learning aims to transform network models into feature representations for predicting network attributes, playing a crucial role in deploying and designing networks for real-world appli…
AttributeGraph Representation LearningRepresentation LearningGraph Persistence goes Spectral
Including intricate topological information (e.g., cycles) provably enhances the expressivity of message-passing graph neural networks (GNNs) beyond the Weisfeiler-Leman (WL) hierarchy. Consequently, Persistent Homology …
Graph Representation LearningRepresentation LearningPositional Encoding meets Persistent Homology on Graphs
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional encoding (PE) and Persistent Homology (PH) …
Graph ClassificationGraph Representation LearningInductive BiasOut-of-Distribution Generalization+2Studying and Improving Graph Neural Network-based Motif Estimation
Graph Neural Networks (GNNs) are a predominant method for graph representation learning. However, beyond subgraph frequency estimation, their application to network motif significance-profile (SP) prediction remains unde…
Graph GenerationGraph Neural NetworkGraph Representation LearningRepresentation Learning+1Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation 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 LearningSimple yet Effective Graph Distillation via Clustering
Despite plentiful successes achieved by graph representation learning in various domains, the training of graph neural networks (GNNs) still remains tenaciously challenging due to the tremendous computational overhead ne…
ClusteringGraph Representation LearningGraph SamplingNode ClassificationConvexified Message-Passing Graph Neural Networks
Graph Neural Networks (GNNs) have become prominent methods for graph representation learning, demonstrating strong empirical results on diverse graph prediction tasks. In this paper, we introduce Convexified Message Pass…
Graph Representation LearningRepresentation LearningSupervised Graph Contrastive Learning for Gene Regulatory Network
Graph representation learning is effective for obtaining a meaningful latent space utilizing the structure of graph data and is widely applied, including biological networks. In particular, Graph Contrastive Learning (GC…
Contrastive LearningData AugmentationGraph Representation LearningRepresentation LearningMini-Game Lifetime Value Prediction in WeChat
The LifeTime Value (LTV) prediction, which endeavors to forecast the cumulative purchase contribution of a user to a particular item, remains a vital challenge that advertisers are keen to resolve. A precise LTV predicti…
Graph Representation LearningPredictionRepresentation LearningValue predictionLOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by their high sensitivity to hyperparameter…
Graph Representation LearningRepresentation LearningRadiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection
Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data incl…
Alzheimer's Disease DetectionClassificationGraph Representation LearningRepresentation LearningLarge Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification
The use of large language models (LLMs) as feature enhancers to optimize node representations, which are then used as inputs for graph neural networks (GNNs), has shown significant potential in graph representation learn…
Graph Representation LearningLanguage ModelingLanguage ModellingLarge Language Model+1DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update
Most of the dynamic graph representation learning methods involve dividing a dynamic graph into discrete snapshots to capture the evolving behavior of nodes over time. Existing methods primarily capture only local or glo…
Graph Representation LearningMeta-LearningRepresentation LearningThe Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
Graph Neural Networks (GNNs) address two key challenges in applying deep learning to graph-structured data: they handle varying size input graphs and ensure invariance under graph isomorphism. While GNNs have demonstrate…
Graph Representation LearningRepresentation Learning