{"product_id":"graphbased-representations-in-pattern-recognition-9783031941382","title":"GraphBased Representations in Pattern Recognition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- \u003cstrong\u003eCybersecurity based on Graph models\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- A Modular Triple Exchange Co-learning Framework for Anomaly Detection in Scarcely Labeled Graph Data.\u003c\/p\u003e\u003cp\u003e.- Advanced Malware Detection in Code Repositories Using Graph Neural Network.\u003c\/p\u003e\u003cp\u003e.- Resistance Distance Guided Node Injection Attack on Graph Neural Network.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eGraph based bioinformatics\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- Gene Co-Expression Networks Are Poor Proxies for Expert-Curated Gene Regulatory Networks.\u003c\/p\u003e\u003cp\u003e.- Graph Neural Network Based on Molecular and Pharmacophoric Features for Drug Design Applications.\u003c\/p\u003e\u003cp\u003e.- Graph-Based Representations of Almost Constant Graphs for Nanotoxicity Prediction.\u003c\/p\u003e\u003cp\u003e.- Label Modulated Dynamic Graph Convolution for Subcellular Structure Segmentation from Nanoscopy Image.\u003c\/p\u003e\u003cp\u003e.- Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models.\u003c\/p\u003e\u003cp\u003e.- Graph Neural Networks for Multimodal Brain Connectivity Analysis in Multiple Sclerosis.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eGraph similarities and graph patterns\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.-  A Geometric Perspective on Graph Similarity Learning using Convex Hulls.\u003c\/p\u003e\u003cp\u003e.- VF-GPU: Exploiting Parallel GPU Architectures to Solve Subgraph Isomorphis.\u003c\/p\u003e\u003cp\u003e.- Grammatical Path Network: Unveiling Cycles Through Path Computation.\u003c\/p\u003e\u003cp\u003e.- Deep QMiner: Towards a generalized DeepQ-Learning Approach for Graph Pattern Mining.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eGNN: shortcomings and solutions\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- An Empirical Investigation of Shortcuts in Graph Learning.\u003c\/p\u003e\u003cp\u003e.-  A General Sampling Framework for Graph Convolutional Network Training.\u003c\/p\u003e\u003cp\u003e.- Fusion of GNN and GBDT Models for Graph and Node Classification.\u003c\/p\u003e\u003cp\u003e.- Harnessing GraphSAGE for Learning Representations of Massive Transactional networks.\u003c\/p\u003e\u003cp\u003e.- Entropy-Guided Graph Clustering via Rényi Optimization.\u003c\/p\u003e\u003cp\u003e.- \u003cstrong\u003eGraph learning and computer vision\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e.- Exploring a Graph Regression Problem in River Networks.\u003c\/p\u003e\u003cp\u003e.- Saliency Matters: from nodes to objects.\u003c\/p\u003e\u003cp\u003e.- Hierarchical super-pixels graph neural networks for image semantic segmentation.\u003c\/p\u003e\u003cp\u003e.- Lifting some Secrets about Contrast Pyramids.\u003c\/p\u003e\u003cp\u003e.- An Evolution Equation Involving the Generalized Biased Infinity Laplacian on Graphs.\u003c\/p\u003e\u003cp\u003e.- Doc2Graph-X: A Multilingual Graph-Based Framework for Form Understanding.\u003c\/p\u003e\u003cp\u003e.- VisHubGAT: Visible Connectivity and Hub Nodes for Multimodal Entity Extraction.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195489149271,"sku":"9783031941382","price":104.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/graphbased-representations-in-pattern-recognition-9783031941382","provider":"Book Curl","version":"1.0","type":"link"}