Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction
Predicting the next mobile app a user will launch is essential for proactive mobile services. Yet accurate prediction remains challenging in real-world settings, where user intent can shift rapidly within short sessions …
Graph LearningCan LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs
Text-attributed graphs (TAGs) enhance graph learning by integrating rich textual semantics and topological context for each node. While boosting expressiveness, they also expose new vulnerabilities in graph learning thro…
Graph LearningDeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns
Advanced Persistent Threats (APTs) are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning (DRL) enables autonomous cyber defense, its decisions are often opaque and …
Reinforcement LearningGraph LearningAdversarial Attacks on Locally Private Graph Neural Networks
Graph neural network (GNN) is a powerful tool for analyzing graph-structured data. However, their vulnerability to adversarial attacks raises serious concerns, especially when dealing with sensitive information. Local Di…
Adversarial RobustnessGraph Neural NetworkGraph LearningGraph-Aware Stealthy Poison-Text Backdoors for Text-Attributed Graphs
Modern graph learning systems often combine links with text, as in citation networks with abstracts or social graphs with user posts. In such systems, text is usually easier to edit than graph structure, which creates a …
Graph LearningAttack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and financial platforms. To comply with privacy regulations like the GDPR, CCP…
Graph LearningLLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs
Text-rich graphs, which integrate complex structural dependencies with abundant textual information, are ubiquitous yet remain challenging for existing learning paradigms. Conventional methods and even LLM-hybrids compre…
Graph LearningMultimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical functional brain connectivity and subtle structural alterations. rs-fMRI has been widely used to identify disruptions in …
Graph LearningPiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing
A comprehensive understanding of heat transport is essential for optimizing various mechanical and engineering applications, including 3D printing. Recent advances in machine learning, combined with physics-based models,…
Transfer LearningGraph LearningA Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
Financial fraud detection in transaction networks involves modeling sparse anomalies, dynamic patterns, and severe class imbalance in the presence of temporal drift in the data. In real-world transaction systems, a suspi…
Fraud DetectionGraph LearningExploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the interactions of the underlying subnetwo…
Graph LearningHELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchies where instances belong to multiple br…
Hierarchical Multi-label ClassificationMulti-Label Image ClassificationGraph LearningGNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the…
Graph LearningMultimodal Graph Representation Learning with Dynamic Information Pathways
Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requires both adaptive intra-modal message pa…
Graph Representation LearningNode ClassificationLink PredictionGraph LearningDetecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning
Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contradict the claims they are cited for. Curr…
Knowledge DistillationSemantic SimilarityGraph LearningGraph2Video: Leveraging Video Models to Model Dynamic Graph Evolution
Dynamic graphs are common in real-world systems such as social media, recommender systems, and traffic networks. Existing dynamic graph models for link prediction often fall short in capturing the complexity of temporal …
Link PredictionGraph LearningDetecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method
The proliferation of fake reviews, often produced by organized groups, undermines consumer trust and fair competition on online platforms. These groups employ sophisticated strategies that evade traditional detection met…
Computational EfficiencyGraph LearningGraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where multimodal entities are connected throug…
Recommendation SystemsGraph LearningHide and Find: A Distributed Adversarial Attack on Federated Graph Learning
Federated Graph Learning (FedGL) is vulnerable to malicious attacks, yet developing a truly effective and stealthy attack method remains a significant challenge. Existing attack methods suffer from low attack success rat…
Federated LearningAdversarial AttackGraph LearningTFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction
Dynamic link prediction plays a crucial role in diverse applications including social network analysis, communication forecasting, and financial modeling. While recent Transformer-based approaches have demonstrated promi…
Dynamic Link PredictionGraph Learning