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Network Analysis Resources & Updates

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Посты канала
📃 Artificial Intelligence–Driven Graph Methods for Microbiome Research: A Systematic Review 📘 Journal: WIREs Mechanisms of
📃 Artificial Intelligence–Driven Graph Methods for Microbiome Research: A Systematic Review 📘 Journal: WIREs Mechanisms of Disease (I.F. = 5.4) 🗓 Publish year: 2026 🧑‍💻 Authors: Cedrique Shum-Tim, Paromita Islam, Jacqueline L. Boyajian, Ahmed Abosalha, ... 🏢 Universities: McGill University, Canada 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #microbiome #AI #graph_neural_networks #gnn

2
📽 Graph Theory for Visual Learners 🎞 Watch ⚡️Channel: @ComplexNetworkAnalysis #video #basics
288
3
📄 From graphs to qubits: a critical review of quantum graph neural networks 📕 Journal: Neural Computing and Applications (I+2
📄 From graphs to qubits: a critical review of quantum graph neural networks 📕 Journal: Neural Computing and Applications (I.F.=4.5) 🗓 Publish year: 2026 🧑‍💻 Authors: Andrea Ceschini, Francesco Mauro, Francesca De Falco, Alessandro Sebastianelli, ... 🏢Universities: University of Rome “La Sapienza” & University of Sannio & European Space Agency, Italy 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #qubits #gnn
544
4
📃 A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein–Protein Interaction Detection 📕
📃 A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein–Protein Interaction Detection 📕 Journal: International Journal of Molecular Sciences (I.F. = 5.6) 🗓 Publish year: 2026 🧑‍💻 Author: Kamal Taha 🏢 Affiliations: Khalifa University, United Arab Emirates 📎 Study the paper 📲 Channel: @Bioinformatics #review #machinelearning #proteomics #PPI
338
5
📑 Graph designs for deep learning–based multi-omics integration 📓 Journal: Briefings in Bioinformatics (I.F.=7.3) 🗓 Publis+1
📑 Graph designs for deep learning–based multi-omics integration 📓 Journal: Briefings in Bioinformatics (I.F.=7.3) 🗓 Publish year: 2026 🧑‍💻Authors: Muhtasim Noor Alif, Khandakar Tanvir Ahmed, Sudipto Baul, Wei Zhang 🏢University: University of Central Florida, USA 📎 Study the paper 📲Channel: @Bioinformatics #review #multiomics #graph #gnn #deeplearning
389
6
📃 A Survey on GNN-Based Link Prediction: Techniques, Applications, and Challenges 📔 Journal: WIREs Data Mining and Knowledg
📃 A Survey on GNN-Based Link Prediction: Techniques, Applications, and Challenges 📔 Journal: WIREs Data Mining and Knowledge Discovery (I.F.=15) 🗓 Publish year: 2026 🧑‍💻Authors: Chengcheng Sun, Yajie Song, Cheng Zhai, Jiayun Tian, Jia Yang, Xiaobin Rui, Jian Zhang, Zhixiao Wang, Philip S. Yu 🏢Universities: China University of Mining and Technology, China – University of Illinois Chicago, USA 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #link_prediction #gnn
450
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📑 A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods 📘 Jou+1
📑 A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods 📘 Journal: ACM Computing Surveys (🔥I.F.=30.4) 🗓 Publish year: 2026 🧑‍💻 Authors: Amir Sheikhahmadi, Laleh Tafakori, Mahdi Jalili 🏢 University: RMIT University, Melbourne, Australia 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #influential_node
484
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📃 From prior knowledge to data-informed models: a review of Boolean network inference 📓 Journal: Briefings in Bioinformatics (I.F.=7.7) 🗓 Publish year: 2026 🧑‍💻 Authors: Pierre Klemmer, Ahmed Abdelmonem Hemedan, Reinhard Schneider, Marek Ostaszewski 🏢 Universities: University of Luxembourg – Luxembourg Centre for Systems Biomedicine (LCSB) & Luxembourg Institute of Health (LIH), Luxembourg 📎 Study the paper: https://doi.org/10.1093/bib/bbag499 ⚡️Channel: @ComplexNetworkAnalysis #review #systemsbiology #boolean_network
3 097
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📹 Graph Reconstruction 🎞 Watch ⚡️Channel: @ComplexNetworkAnalysis #video #graph #reconstruction
443
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📑 Explainable AI for Graph-Based Learning: A Survey Beyond Graph Neural Networks 🗓 Publish year: 2026 🧑‍💻Authors: Margari
📑 Explainable AI for Graph-Based Learning: A Survey Beyond Graph Neural Networks 🗓 Publish year: 2026 🧑‍💻Authors: Margarita Bugueño, Russa Biswas, Gerard de Melo 🏢Universities: Hasso Plattner Institute (HPI) / University of Potsdam, Germany - Aalborg University, Denmark 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #explainable #ai #gnn
532
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📃 Multilayer public transport networks 🗓 Publish year: 2026 🧑‍💻 Authors: Tina Šfiligoj, Renzo Massobrio, Oded Cats 🏢 Uni
📃 Multilayer public transport networks 🗓 Publish year: 2026 🧑‍💻 Authors: Tina Šfiligoj, Renzo Massobrio, Oded Cats 🏢 Universities: University of Ljubljana, Slovenia — University of Antwerp, Belgium — Delft University of Technology, The Netherlands 🔍 Highlights: A structured review of multilayer network approaches in public transportation, including network modelling, resilience analysis, service planning, and a proposed taxonomy and research agenda for future research. 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #multilayer #transportation
581
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📚 Network model selection: A review of methods 🏛Publisher: Springer Nature — SpringerBriefs 🗓 Publication year: 2026 🧑‍💻
📚 Network model selection: A review of methods 🏛Publisher: Springer Nature — SpringerBriefs 🗓 Publication year: 2026 🧑‍💻Author: Zoran Levnajić 🏢Affiliation: Faculty of Information Studies in Novo mesto, Slovenia 🔍 This review provides a systematic overview of methods for selecting the network model that best explains a given complex network. It categorizes existing approaches, discusses their principles and software availability, and highlights future directions in network model selection. 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #analysis #model
566
13
📄 A Primer on Bayesian Neural Networks: Review and Debates 📙 Journal: Statistical Science (Impact Factor: 2.32) 🗓 Publish+1
📄 A Primer on Bayesian Neural Networks: Review and Debates 📙 Journal: Statistical Science (Impact Factor: 2.32) 🗓 Publish year: 2026 🧑‍💻Authors: Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, ... 🏢Universities: Centre Inria de l’Universit´e Grenoble Alpes & Criteo AI Lab, France - Helmholtz AI, Munich, Gremany 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #bayesian #neural_network
538
14
📃 Graph-based drug–target interaction modeling: from representation learning to output-driven drug discovery 📗 Briefings in
📃 Graph-based drug–target interaction modeling: from representation learning to output-driven drug discovery 📗 Briefings in Bioinformatics (Impact Factor: 7.3, Q1) 🗓 2026 🧑‍💻 Thanh Nguyen , Hien Minh To , Duy Anh Nguyen ,... 🏢 Nanyang Biologics, Singapore 📎 Study the paper ⚡️ @ComplexNetworkAnalysis #review #drug_target #bipartite
2 928
15
📹 Graph Theory Workshop 🎞 Watch ⚡️ @ComplexNetworkAnalysis #video #graph
554
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📄 Graph Contrastive Learning: A Comprehensive Review of Methodologies, Applications, and Future Directions 📕 IEEE Access (I
📄 Graph Contrastive Learning: A Comprehensive Review of Methodologies, Applications, and Future Directions 📕 IEEE Access (Impact Factor: 4.2, Q2) 🗓 2026 🧑‍💻 Nazmul Hossain, Abdullah Al Thaki, Md. Mamun-Or-Rashid, Md. Mosaddek Khan 🏢 University of Dhaka, Bangladesh 📎 Study the paper ⚡️ @ComplexNetworkAnalysis #review #ContrastiveLearning #GNN #SelfSupervisedLearning #GraphNeuralNetworks #RepresentationLearning #MachineLearning
539
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📹 Graph Engineering 🎞 Watch ⚡️Channel: @ComplexNetworkAnalysis #video #graph_engineering
566
18
📃 Introduction to Graph Neural Networks for Machine Learning Engineers 📘 Journal: IEEE Access (I.F.=3.6) 🗓 Publish year: 2
📃 Introduction to Graph Neural Networks for Machine Learning Engineers 📘 Journal: IEEE Access (I.F.=3.6) 🗓 Publish year: 2026 🧑‍💻Authors: James Tanis, Chris Giannella, Adrian Mariano, ... 🏢Universities: The MITRE Corporation - National Cancer Institute Shady Grove Campus, United States 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #graph_neural_network #machine_learning
502
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📄 Graph Neural Networks Applications Across Domains: All Insights You Need 📕 Journal: arXiv preprint (not yet peer-reviewed+4
📄 Graph Neural Networks Applications Across Domains: All Insights You Need 📕 Journal: arXiv preprint (not yet peer-reviewed) 🗓 Publish year: 2026 🧑‍💻Authors: Abderaouf Bahi 🏢University: Chadli Bendjedid University of El Tarf, Algeria 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #GNN #GraphNeuralNetworks #Survey
629
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📑 A review on the use of complex networks in science education research 🗓 Publish year: 2026 🧑‍💻Authors: Paula Tuz´on, Ju
📑 A review on the use of complex networks in science education research 🗓 Publish year: 2026 🧑‍💻Authors: Paula Tuz´on, Juan Garc´ıa-Castillo, and Juan Fern´andez-Gracia 🏢University: Universidad de Valencia, Spain 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #science #education
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