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[null,"1.","Record Nr.",null,"UNISA996664551803316"]
[null,null,"Autore",null,"Brun Luc"]
[null,null,"Titolo",null,"Graph-Based Representations in Pattern Recognition : 14th IAPR-TC-15 International Workshop, GbRPR 2025, Caen, France, June 25–27, 2025, Proceedings / / edited by Luc Brun, Vincenzo Carletti, Sébastien Bougleux, Benoît Gaüzère"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-94139-X"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (464 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15727"]
[null,null,"Disciplina",null,"006.4"]
[null,null,"Soggetti",null,"Pattern recognition systems"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Discrete mathematics"]
[{"$nulls":4},"Computer graphics"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Artificial intelligence - Data processing"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Discrete Mathematics in Computer Science"]
[{"$nulls":4},"Computer Graphics"]
[{"$nulls":4},"Data Science"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- Cybersecurity based on Graph models.  -- A Modular Triple Exchange Co-learning Framework for Anomaly Detection in Scarcely Labeled Graph Data.  -- Advanced Malware Detection in Code Repositories Using Graph Neural Network.  -- Resistance Distance Guided Node Injection Attack on Graph Neural Network.  -- Graph based bioinformatics.  -- Gene Co-Expression Networks Are Poor Proxies for Expert-Curated Gene Regulatory Networks.  -- Graph Neural Network Based on Molecular and Pharmacophoric Features for Drug Design Applications.  -- Graph-Based Representations of Almost Constant Graphs for Nanotoxicity Prediction.  -- Label Modulated Dynamic Graph Convolution for Subcellular Structure Segmentation from Nanoscopy Image.  -- Insights on Using Graph Neural Networks "]
[{"$nulls":4},"for Sulcal Graphs Predictive Models.  -- Graph Neural Networks for Multimodal Brain Connectivity Analysis in Multiple Sclerosis.  -- Graph similarities and graph patterns.  -- A Geometric Perspective on Graph Similarity Learning using Convex Hulls.  -- VF-GPU: Exploiting Parallel GPU Architectures to Solve Subgraph Isomorphis.  -- Grammatical Path Network: Unveiling Cycles Through Path Computation.  -- Deep QMiner: Towards a generalized DeepQ-Learning Approach for Graph Pattern Mining.  -- GNN: shortcomings and solutions.  -- An Empirical Investigation of Shortcuts in Graph Learning.  -- A General Sampling Framework for Graph Convolutional Network Training.  -- Fusion of GNN and GBDT Models for Graph and Node Classification.  -- Harnessing GraphSAGE for Learning Representations of Massive Transactional networks.  -- Entropy-Guided Graph Clustering via Rényi Optimization.  -- Graph learning and computer vision.  -- Exploring a Graph Regression Problem in River Networks.  -- Saliency Matters: from nodes to objects.  -- Hierarchical super-pixels graph neural networks for image semantic segmentation.  -- Lifting some Secrets about Contrast Pyramids.  -- An Evolution Equation Involving the Generalized Biased Infinity Laplacian on Graphs.  -- Doc2Graph-X: A Multilingual Graph-Based Framework for Form Understanding.  -- VisHubGAT: Visible Connectivity and Hub Nodes for Multimodal Entity Extraction."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 14th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2025, held in Caen, France, in June 2025. The 25 full papers presented here were carefully reviewed and selected from 33 submissions. They are organized as per the following topical sections: Cybersecurity based on Graph models; Graph based bioinformatics; Graph similarities and graph patterns; GNN: shortcomings and solutions; Graph learning and computer vision."]