Papers Graph Representation Learning
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Explainable Graph Representation Learning via Graph Pattern Analysis
Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous work has explored model-level and instance…
Graph Representation LearningGraph LearningCross-View Topology-Aware Graph Representation Learning
Graph classification has gained significant attention due to its applications in chemistry, social networks, and bioinformatics. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they ofte…
Graph Representation LearningGraph ClassificationContrastive LearningT2T-LA: A Topology-to-Topology LLM Agent for Graph Learning with Neither Feature Access nor Task Knowledge
Graph learning aims to convert data into graph representations, which are fundamental to many problems in machine learning for CAD, where circuits, layouts, designs, and optimization states are often modeled as graph-str…
Graph Representation LearningGraph LearningGeneralized Graph Transformer Variational Autoencoder
Graph link prediction has long been a central problem in graph representation learning in both network analysis and generative modeling. Recent progress in deep learning has introduced increasingly sophisticated architec…
Graph Representation LearningLink PredictionIVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder
Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing imputation methods often fail to capture…
Graph Representation LearningPretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer's Disease Prediction
Early and accurate detection of Alzheimer's disease (AD) is crucial for enabling timely intervention and improving outcomes. However, developing reliable machine learning (ML) models for AD diagnosis is challenging due t…
Graph Representation LearningSynthetic Data GenerationTransfer LearningDS-Span: Single-Phase Discriminative Subgraph Mining for Efficient Graph Embeddings
Graph representation learning seeks to transform complex, high-dimensional graph structures into compact vector spaces that preserve both topology and semantics. Among the various strategies, subgraph-based methods provi…
Graph Representation LearningGraph EmbeddingFairness-Aware Graph Representation Learning with Limited Demographic Information
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning methods have been proposed in recent years. …
Graph Representation LearningGraph LearningVISAGNN: Versatile Staleness-Aware Efficient Training on Large-Scale Graphs
Graph Neural Networks (GNNs) have shown exceptional success in graph representation learning and a wide range of real-world applications. However, scaling deeper GNNs poses challenges due to the neighbor explosion proble…
Graph Representation LearningFairGSE: Fairness-Aware Graph Neural Network without High False Positive Rates
Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also amplifies biases inherent in graph topology,…
Graph Representation LearningGraph Neural NetworkLanguage-Guided Graph Representation Learning for Video Summarization
With the rapid growth of video content on social media, video summarization has become a crucial task in multimedia processing. However, existing methods face challenges in capturing global dependencies in video content …
Graph Representation LearningRelational ReasoningVideo SummarizationMG-HGNN: A Heterogeneous GNN Framework for Indoor Wi-Fi Fingerprint-Based Localization
Received signal strength indicator (RSSI) is the primary representation of Wi-Fi fingerprints and serves as a crucial tool for indoor localization. However, existing RSSI-based positioning methods often suffer from reduc…
Graph Representation LearningGraph Neural NetworkSpectral Neural Graph Sparsification
Graphs are central to modeling complex systems in domains such as social networks, molecular chemistry, and neuroscience. While Graph Neural Networks, particularly Graph Convolutional Networks, have become standard tools…
Graph Representation LearningCommunity DetectionGraph LearningToward Robust Signed Graph Learning through Joint Input-Target Denoising
Signed Graph Neural Networks (SGNNs) are widely adopted to analyze complex patterns in signed graphs with both positive and negative links. Given the noisy nature of real-world connections, the robustness of SGNN has als…
Graph Representation LearningGraph structure learningInformation ExtractionGraph LearningRandom Search Neural Networks for Efficient and Expressive Graph Learning
Random walk neural networks (RWNNs) have emerged as a promising approach for graph representation learning, leveraging recent advances in sequence models to process random walks. However, under realistic sampling constra…
Graph Representation LearningGraph LearningEnhancing Graph Classification Robustness with Singular Pooling
Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remains underexplored compared to node classif…
Graph Representation LearningAdversarial RobustnessGraph ClassificationNode ClassificationUnifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning due to their ability to model diverse node interactions. However, existing GTs often rely on intricate architectural designs …
Graph Representation LearningSTEMS: Spatial-Temporal Enhanced Safe Multi-Agent Coordination for Building Energy Management
Building energy management is essential for achieving carbon reduction goals, improving occupant comfort, and reducing energy costs. Coordinated building energy management faces critical challenges in exploiting spatial-…
Multi-agent Reinforcement LearningGraph Representation LearningInferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
Introduction Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing drug development. Existing in-silico methods use direct sequence embeddings from Prote…
Graph Representation LearningNode ClassificationLink PredictionMIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation
With the rapid expansion of scientific literature, scholars increasingly demand precise and high-quality paper recommendations. Among various recommendation methodologies, graph-based approaches have garnered attention b…
Graph Representation Learning