Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing trigger generators are often simplistic in structure and overly relia…
Contrastive LearningGraph LearningGraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powerful GNNs, the "pre-training, adaptation…
Node ClassificationGraph LearningRelieving the Over-Aggregating Effect in Graph Transformers
Graph attention has demonstrated superior performance in graph learning tasks. However, learning from global interactions can be challenging due to the large number of nodes. In this paper, we discover a new phenomenon t…
Graph LearningDeep Learning on Real-World Graphs
Graph Neural Networks (GNNs) have become a central tool for learning on graph-structured data, yet their applicability to real-world systems remains limited by key challenges such as scalability, temporality, directional…
Graph LearningTransferable Graph Learning for Transmission Congestion Management via Busbar Splitting
Network topology optimization (NTO) via busbar splitting can mitigate transmission grid congestion and reduce redispatch costs. However, solving this mixed-integer nonlinear problem for large-scale systems in near-real-t…
Graph Neural NetworkGraph LearningRobustness Verification of Graph Neural Networks Via Lightweight Satisfiability Testing
Graph neural networks (GNNs) are the predominant architecture for learning over graphs. As with any machine learning model, an important issue is the detection of attacks, where an adversary can change the output with a …
Adversarial RobustnessImage ClassificationGraph LearningExplainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing
Financial anomalies arise from heterogeneous mechanisms -- price shocks, liquidity freezes, contagion cascades, and momentum reversals -- yet existing detectors produce uniform scores without revealing which mechanism is…
Anomaly DetectionGraph LearningRobustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a comprehensive understanding of their robustness remains elusive. Current evalua…
Graph LearningLearning Time-Varying Graphs from Incomplete Graph Signals
This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unified non-convex optimization framework to s…
Graph LearningGraph Learning Is Suboptimal in Causal Bandits
We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying the reward's parents and then applying cla…
Graph LearningHephaestus: Mixture Generative Modeling with Energy Guidance for Large-scale QoS Degradation
We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastructures and distributed ML systems, wher…
Reinforcement LearningGraph LearningSSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning
Vision-language models (VLMs) have shown remarkable abilities by integrating large language models with visual inputs. However, they often fail to utilize visual evidence adequately, either depending on linguistic priors…
Self-Supervised LearningReinforcement LearningGraph LearningStealthy Dual-Trigger Backdoors: Attacking Prompt Tuning in LM-Empowered Graph Foundation Models
The emergence of graph foundation models (GFMs), particularly those incorporating language models (LMs), has revolutionized graph learning and demonstrated remarkable performance on text-attributed graphs (TAGs). However…
Graph LearningAGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining
Nanopore sequencing enables real-time long-read DNA sequencing with reads exceeding 10 kilobases, but inherent error rates of 12-15 percent present significant computational challenges for read alignment. The critical se…
Graph Neural NetworkGraph LearningMulti-View Semi-Supervised Label Distribution Learning with Local Structure Complementarity
Label distribution learning (LDL) is a paradigm that each sample is associated with a label distribution. At present, the existing approaches are proposed for the single-view LDL problem with labeled data, while the mult…
Graph LearningMessage Passing on the Edge: Towards Scalable and Expressive GNNs
Graph neural networks (GNNs) are widely used in graph learning and most architectures propagate information by passing messages between vertices. In this work, we shift our attention to GNNs that perform message passing …
Graph LearningT3former: Temporal Graph Classification with Topological Machine Learning
Temporal graph classification plays a critical role in applications such as cybersecurity, brain connectivity analysis, social dynamics, and traffic monitoring. Despite its significance, this problem remains underexplore…
Graph ClassificationLink PredictionGraph LearningH4G: Unlocking Faithful Inference for Zero-Shot Graph Learning in Hyperbolic Space
Text-attributed graphs are widely used across domains, offering rich opportunities for zero-shot learning via graph-text alignment. However, existing methods struggle with tasks requiring fine-grained pattern recognition…
Zero-Shot LearningGraph LearningLearning the Structure of Connection Graphs
Connection graphs (CGs) extend traditional graph models by coupling network topology with orthogonal transformations, enabling the representation of global geometric consistency. They play a key role in applications such…
Graph LearningEvent-Aware Prompt Learning for Dynamic Graphs
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural networks (DGNNs) have emerged as popular so…
Graph Learning