Graph Neural Networks

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Graph Neural Networks

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Graph Representations

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Graph Representation Learning

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Heterogeneous Graph Transformers

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Graph Foundation Model

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Maximum Independent Set

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Text-Attributed Graph

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Message Passing Neural Networks

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Subgraphs

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Gnn-Based Detectors

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Graph Theory

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Exponential Random Graph Models

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EdgeFlow: Edge-Map Augmented VLM-Based Flowchart Processing for Industrial Requirements Engineering
May 26, 2026Zhifei Dou, Shabnam Hassani, Ou WeiDiagrams Ava-Vlm
GraphReview: Scientific Paper Evaluation via LLM-Based Graph Message Passing
May 26, 2026Pujun Zheng, Wanying Ren, Jiacheng Yao +2Peer Review Information Retrieval Metrics
Learning Dynamic Graph Representations through Timespan View Contrasts
May 26, 2026Yiming Xu, Zhen Peng, Bin Shi +2Temporal Graphs Generalist Graph Anomaly Detection
TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
May 26, 2026Yiming Xu, Bin Shi, Bo Dong +3Text-Attributed Graph Anti-Money Laundering
On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
May 26, 2026Malte Luttermann, Ralf Möller, Marcel GehrkeFactor Graphs Factor
Generalist Graph Anomaly Detection via Prototype-Based Distillation
May 26, 2026Yiming Xu, Zihan Chen, Zhen Peng +4Generalist Graph Anomaly Detection Anomaly Detection
RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
May 26, 2026Yali Fink, Ido Ben-Yair, Lars Ruthotto +1Spectral Preconditioning Graph Laplacians
Adversarial Training for Robust Coverage Network under Worst-case Facility Losses
May 26, 2026Changhao Miao, Yuntian Zhang, Tongyu Wu +2Finite-Sample Coverage Guarantees Adversarial Training
DDGAD: Disagreement-Driven Graph Anomaly Detection via Adapt-Then-Combine
May 26, 2026Yuxin Yang, Limei Hu, Feng ChenGeneralist Graph Anomaly Detection Graph Representation Learning
Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
May 25, 2026Derek Regier, Andrew Polyak, Aresh Dadlani +1Temporal Graph Neural Networks Zero-Shot Link Prediction
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
May 25, 2026Zhishuai Guo, Wenhan Wu, Chen Chen +3Federated Graph Learning Graph Neural Networks
L2IR: Revealing Latent Intent in Graph Fraud Detection
May 25, 2026Jinsheng Guo, Zhenhao Weng, Yibo Liu +2Generalist Graph Anomaly Detection Fraud Detection
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
May 25, 2026Ziyu Zheng, Yaming Yang, Ziyu Guan +2Subgraphs Model Pretraining
Invariant-Based Weight Sharing for Message Passing
May 25, 2026Florian SeiffarthMessage Passing Neural Networks
Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection
May 25, 2026Gabriel Masella, Giuseppe Alessio D'Inverno, Max Goldsmith +1Message Passing Neural Networks Graph Neural Networks
Closed-Form Node Classification with Exact Graph Unlearning
May 25, 2026Aditya Gaur, Charu SharmaGraph Neural Networks Propagation
ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
May 25, 2026VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin KumarMedical Image Classification Graph Representation Learning
SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
May 25, 2026Shubhajit Roy, Anirban DasguptaTemporal Graph Neural Networks Gnn-Based Detectors
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
May 25, 2026Nil Ayday, Mahalakshmi Sabanayagam, Debarghya GhoshdastidarGraph Neural Networks Exponential Random Graph Models
Learning manifold diffusion semigroups from graph transition matrices
May 25, 2026Xiuyuan Cheng, Nan WuGraph Laplacians Graph Representation Learning
Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization
May 24, 2026Yudong W. Xu, Wenhao Li, Xiaoyu Wang +2Constraint Satisfaction Multimodal Diffusion Transformers
Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation
May 24, 2026Zichao Yue, Zhiru ZhangGraph Neural Networks Propagation
GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
May 24, 2026Ziyi Wang, Dongyang KuangHuman Brain Networks Emotion Recognition
Clustering as Reasoning: A $k$ -Means Interpretation of Chain-of-Thought Graph Learning
May 24, 2026Xuanting Xie, Zhaochen Guo, Bingheng Li +4Graph Representation Learning Chain-of-Thought Reasoning
Complement Submodular Information Measures for Balanced and Robust Data Selection
May 23, 2026Rishabh IyerSubmodular Maximization Geometry-Aware Uncertainty Coresets
Geometry-Aware Tabular Diffusion
May 23, 2026David Turtora ZagardoGraph Neural Networks
Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs
May 23, 2026Hao Yan, Xuanru Wang, Jun Yin +3Graph Foundation Model Dual-Path Framework
Affinity Graph Connectivity in Convex Clustering
May 23, 2026Sam Rosen, Jason XuExponential Random Graph Models Convex Sets
Graph Mamba Survival Analysis Based on Topology-Aware ordering
May 23, 2026Yuanfang Chen, Peiqiang Yan, Yuntao Shou +2Bidirectional Mamba Computational Pathology
Gaussian Rank-Based Neighborhood Degree for Graph Neural Networks in Image Classification
May 23, 2026Rafael Mendonça Duarte, Jean Roberto Ponciano, Lucas Pascotti ValemGraph Neural Networks Multiple Centrality Measures
Treatment Effect Estimation with Differentiated Networked Effect on Graph Data
May 23, 2026Xiaofeng Lin, Han Bao, Hisashi KashimaHeterogeneous Treatment Effects Complex Networks
Incorporating Deep Learning Design in Database Queries
May 22, 2026Yuval Lev Lubarsky, Dean Light, Boaz Berger +2Relational Deep Learning Deep Learning
Knowledge Graph Modulated Deep Learning for Limited-Sample Clinical Data Analysis
May 22, 2026Yuwei Xue, Sakib Mostafa, James Zou +5Biomedical Knowledge Graph Graph Representation Learning
Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries
May 22, 2026Dongmin Lee, Anuran Makur, Japneet SinghExponential Random Graph Models Errors
The Communication Complexity of Instant-Runoff Voting
May 22, 2026Élie de Panafieu, François Durand, Jérôme LangMaximum Independent Set Upper Bounds
Learning Dynamic Stability Landscapes in Synchronization Networks
May 22, 2026Christian Nauck, Junyou Zhu, Michael Lindner +1Complex Networks Graph Neural Networks
Relevant Walk Search for Explaining Graph Neural Networks
May 22, 2026Ping Xiong, Thomas Schnake, Michael Gastegger +3Graph Neural Networks Neural Network
S $^3$ GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
May 22, 2026Dai Shi, Luke Thompson, Linhan Luo +4Message Passing Neural Networks Long-Range Temporal Dependencies
Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
May 22, 2026Snir Hordan, Nadav Dym, Tim SeppeltGraph Theory Graph Neural Networks
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
May 22, 2026Hoyun Choi, Junghyo Jo, Deok-Sun LeeExponential Random Graph Models Graph Representation Learning
RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
May 22, 2026Jinyu Yang, Cheng Yang, Junze Chen +4Relational Deep Learning Relational
Self-supervised Adversarial Purification for Graph Neural Networks
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Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion
May 22, 2026Jaejun Lee, Seheon Kim, Joyce Jiyoung WhangKnowledge Graphs Hypergraphs
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
May 22, 2026Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1Ac Optimal Power Flow Smart Grids
Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?
May 21, 2026Pedro C. Vieira, Pedro Ribeiro, Viacheslav BorovitskiyGraph Neural Networks Ensemble
GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
May 21, 2026Ao Li, Shangpeng Yang, Fahao Chen +3Workflow Automation and Optimization Hierarchical Graph Memory
Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
May 21, 2026Yiming Xu, Yixuan Liu, Yuhang Zhang +3Medical Image Classification Multimodal Clinical Data
Implicit Regularization of Mini-Batch Training in Graph Neural Networks
May 21, 2026Clement Wang, Antoine Vialle, Robin Vaysse +1Graph Neural Networks Mini-Batch Stochastic Gradient Descent
Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering
May 21, 2026Zeqiang Xian, Caihui Liu, Yong Zhang +1Granular-Ball Computing Image Clustering
Efficient Higher-order Subgraph Attribution via Message Passing
May 21, 2026Ping Xiong, Thomas Schnake, Grégoire Montavon +2Subgraphs Graph Neural Networks
GHI: Graphormer over Conditioned Hypergraph Incidence for Aspect-Based Sentiment Analysis
May 21, 2026Yu Du, Wenlong Zhu, Xingze Li +3Sentiment Analysis Hypergraphs
GALAR-TemporalNet v2: Anatomy-Guided Dual-Branch Temporal Classification with Bidirectional Mamba and Dual-Graph GCN for Video Capsule Endoscopy -- after competition results
May 21, 2026Jiye Won, Seangmin Lee, Soon Ki JungVideo Capsule Endoscopy Temporal Deep Learning Framework
Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
May 21, 2026Manuel Noah Riesen, Peter Alfred von NiederhäusernLarge Language Model Reasoning Tree-Of-Thoughts
Ultra-High-Definition Image Quality Assessment via Graph Representation Learning
May 21, 2026Shaode Yu, Enqi Chen, Ming Huang +4Image Quality Assessment High-Resolution
The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems
May 21, 2026Yohei NakajimaHierarchical Graph Memory Agentic Systems
Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs
May 21, 2026Yoav Kor Sade, Arvindh Arun, Rishi Puri +2Graph-Llm Joint Graph-Based Retrieval Augmented Generation
Exact Uniform L1 Spacing for Solow-Polasky Diversity on Lines and Ordered Pareto Fronts
May 21, 2026Michael T. M. Emmerich, Mahboubeh Nezhadmoghaddam, Jesús Guillermo Falcón CardonaPareto Frontier Maximum Independent Set
Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
May 20, 2026Aaron Wang, Zihan Zhao, Alan Xia +5High Energy Physics Transformer Attention
EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
May 20, 2026Mansoor Ahmed, Sujin Lee, Umar Khayaz +1Antibody Alphaevolve
Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction
May 20, 2026Tilman Tröster, David Mirkovic, Veronika Oehl +1Cosmology Mixformer

May 26, 2026 · cs.SEJ/K move · Enter open · S save

EdgeFlow: Edge-Map Augmented VLM-Based Flowchart Processing for Industrial Requirements Engineering

Zhifei Dou, Shabnam Hassani, Ou Wei

Huawei Research Canada

Flowcharts are widely used in industrial requirements, but usually remain embedded as static images. Vision Language Models (VLMs) show promise in the conversion of these flowcharts into machine-readable models for RE activities, yet, when directly applied to flowchart conversion, they often fail on topology-critical visual details. To address this, we propose EdgeFlow that augments a VLM's original input with a deterministically extracted Canny edge map-acting as a structural prior-to improve flowchart-to-Mermaid conversion, without requiring annotated training data or domain-specific model fine-tuning. We evaluate EdgeFlow on IndusReqFlow, a dataset sourced from real-world requirements. Compared with off-the-shelf VLMs, EdgeFlow improves node-level F1 by 17.39 percentage points and edge-level F1 by 16.94 percentage points. At the path level, EdgeFlow improves path F1 by 11.06 percentage points, enabling better support for model-based testing. These results demonstrate that EdgeFlow provides a practical, training-free means to improve topology-preserving flowchart-to-Mermaid conversion for industrial RE. Cross-dataset evaluation results on a public synthetic benchmark show no significant improvement; this highlights the need for diverse benchmarks incorporating industrial data for the comprehensive evaluation of future VLM-based RE tools.

Diagrams Ava-Vlm Graph Representations Industrial Topological Data Analysis

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