Papers Graph Reconstruction
“Graph Reconstruction” 태그가 달린 논문 88편 · 필터 해제
EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expe…
Graph ReconstructionRAGRetrievalRetrieval-augmented GenerationReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels
Graph Neural Networks (GNNs) achieve high performance across many applications but function as black-box models, limiting their use in critical domains like healthcare and criminal justice. Explainability methods address…
DenoisingGraph ReconstructionReconstruction AttackELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction
Large language models (LLMs) are widely used as evaluators for open-ended tasks, while previous research has emphasized biases in LLM evaluations, the issue of non-transitivity in pairwise comparisons remains unresolved:…
Graph ReconstructionCausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
Causal discovery for dynamical systems poses a major challenge in fields where active interventions are infeasible. Most methods used to investigate these systems and their associated benchmarks are tailored to determini…
Causal DiscoveryGraph ReconstructionUnveiling and Steering Connectome Organization with Interpretable Latent Variables
The brain's intricate connectome, a blueprint for its function, presents immense complexity, yet it arises from a compact genetic code, hinting at underlying low-dimensional organizational principles. This work bridges c…
Graph ReconstructionRepresentation LearningGRAIN: Exact Graph Reconstruction from Gradients
Federated learning claims to enable collaborative model training among multiple clients with data privacy by transmitting gradient updates instead of the actual client data. However, recent studies have shown the client …
Federated LearningGraph AttentionGraph ReconstructionGraph Inference with Effective Resistance Queries
The goal of graph inference is to design algorithms for learning properties of a hidden graph using queries to an oracle that returns information about the graph. Graph reconstruction, verification, and property testing …
Graph ReconstructionTree DecompositionCLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs
Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph prompting typically use feature prompts to…
Graph ReconstructionNode ClassificationPrompt LearningGraph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mechanism, which largely relies on the avai…
Graph ReconstructionNode ClassificationDual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering
Multi-view clustering is an important machine learning task for multi-media data, encompassing various domains such as images, videos, and texts. Moreover, with the growing abundance of graph data, the significance of mu…
ClusteringGraph ClusteringGraph ReconstructionCoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection
Swift and accurate blood smear analysis is an effective diagnostic method for leukemia and other hematological malignancies. However, manual leukocyte count and morphological evaluation using a microscope is time-consumi…
Diagnosticfeature selectionGraph ReconstructionIdentifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformer
Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes. However, this approach relies heavily on p…
Graph ReconstructionLarge-Scale Targeted Cause Discovery with Data-Driven Learning
We propose a novel machine learning approach for inferring causal variables of a target variable from observations. Our focus is on directly inferring a set of causal factors without requiring full causal graph reconstru…
Causal DiscoveryGraph ReconstructionContrastive Representation Learning for Dynamic Link Prediction in Temporal Networks
Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and tempora…
Contrastive LearningDynamic Link PredictionGraph Neural NetworkGraph Reconstruction+2Graph Neural Network, ChebNet, Graph Convolutional Network, and Graph Autoencoder: Tutorial and Survey
This is a tutorial paper on graph neural networks including ChebNet, graph convolutional network, graph attention network, and graph autoencoder. It starts with Laplacian of graph, graph Fourier transform, and graph conv…
Graph AttentionGraph Neural NetworkGraph ReconstructionBindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions between the molecule and its environment. I…
Graph ReconstructionLanguage ModelingLanguage ModellingExploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling …
Collaborative FilteringGraph GenerationGraph ReconstructionRecommendation SystemsOptimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. Traditional detection methods need to trade off OOD detection and i…
DenoisingGraph ReconstructionRepresentation LearningTemporal Generalization Estimation in Evolving Graphs
Graph Neural Networks (GNNs) are widely deployed in vast fields, but they often struggle to maintain accurate representations as graphs evolve. We theoretically establish a lower bound, proving that under mild conditions…
AttributeGraph ReconstructionGraph Parsing Networks
Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memor…
Graph ClassificationGraph ReconstructionNode ClassificationNode Clustering