Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,121편 · 필터 해제
Dynamic Heterogeneous Graph Representation Learning: A Survey
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entities with complex interactions, posing si…
Graph Representation LearningGraph Neural NetworkBeyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits
Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the immense scale of industrial netlists and…
Graph Representation LearningGraph Neural NetworkRAD: Rule-Augmented Relational Anomaly Detection
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattening can obscure entity identity, schema st…
Graph Representation LearningAnomaly DetectionGraph Representation Learning of Lightweight IoT Ciphers
SIMON and SIMECK belong to a family of Lightweight Cryptographic Algorithms (LCAs) based on the Feistel block cipher, designed for Internet of Things (IoT) devices. As with all Feistel ciphers, they are susceptible to di…
Graph Representation LearningFeature EngineeringHP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…
Graph Representation LearningSelf-Supervised LearningGraph ClassificationGraph RegressionTopoFormer: Topology Meets Attention for Graph Learning
We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is Topo-Scan, a novel module…
Molecular Property PredictionGraph Representation LearningGraph ClassificationGraph LearningGuarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). Among these risks, malware has become a pervasive and destructive threa…
Graph Representation LearningMalware DetectionInstitutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs
Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers. However, forecasting future allocations and modeling future demand remai…
Graph Representation LearningGraph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), e…
Graph Representation LearningSelf-Supervised LearningGraph Neural NetworkTarget-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks
Graph Neural Networks (GNNs) have achieved remarkable performance in graph representation learning, yet their inherent vulnerability to adversarial attacks poses severe security risks. Especially, black-box node injectio…
Graph Representation LearningReinforcement LearningLLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection
Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI. Existing learning-based detection methods struggle to capt…
Graph Representation LearningGraph Neural NetworkMKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction
Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficult setting arises when candidate interactions include proteins that hav…
Graph Representation LearningKnowledge GraphsGraph LearningSAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention. Most existing self-supe…
Graph Representation LearningKnowledge DistillationContinual LearningGraph LearningGNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
Demand forecasting at the bottom of a retail hierarchy requires predicting tens of thousands of correlated long-horizon series across products, stores, and regions. Modern systems must scale across massive catalogs, capt…
Graph Representation LearningA General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks
A Graph Neural Network (GNN) framework for predicting the solvability of finite groups from their Cayley graph representations was introduced in [1]. In the present work, we generalize this approach and develop a propert…
Graph Representation LearningGraph Neural NetworkSwarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems
Collective behavior arises when locally interacting units produce coordinated global organization, from synchronization in dynamical systems to task-relevant information flow on graphs. The central challenge is not only …
Graph Representation LearningBridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogeneity and representation degradation during graph learning. Real-worl…
Graph Representation LearningGraph ClusteringGraph LearningImproving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration
Effective human-robot teamwork requires robots to adapt to partners, situations, and task dynamics from the start of an interaction. In the MATRX Urban Search and Rescue (USAR) environment, people can externalize collabo…
Graph Representation LearningA Machine Learning-Based Framework for Discovering Huntington's Disease Stages: Integrating Graph Representation Learning and clustering to Uncover Progression Dynamics in Longitudinal Enroll-HD Dataset
Huntington's disease (HD) is a progressive brain disorder that gradually affects movement, cognitive function, and behavior. Identifying the stage of the disease accurately and consistently is important for understanding…
Graph Representation LearningPAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
Whilst the vulnerability of graph neural networks (GNNs) to adversarial attacks poses a critical threat to graph representation learning, the understanding of the robust generalization behavior remains a fundamental chal…
Graph Representation LearningAdversarial RobustnessGraph Classification