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Repost from Bioinformatics
📑 Graph designs for deep learning–based multi-omics integration 📓 Journal: Briefings in Bioinformatics (I.F.=7.3) 🗓 Publis
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📑 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

📃 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

📑 A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods 📘 Jou
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📑 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

📃 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

📹 Graph Reconstruction 🎞 Watch ⚡️Channel: @ComplexNetworkAnalysis #video #graph #reconstruction

📑 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

📃 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

📚 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

📄 A Primer on Bayesian Neural Networks: Review and Debates 📙 Journal: Statistical Science (Impact Factor: 2.32) 🗓 Publish
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📄 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

📃 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

📹 Graph Theory Workshop 🎞 Watch ⚡️ @ComplexNetworkAnalysis #video #graph

📄 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

📹 Graph Engineering 🎞 Watch ⚡️Channel: @ComplexNetworkAnalysis #video #graph_engineering

📃 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

📄 Graph Neural Networks Applications Across Domains: All Insights You Need 📕 Journal: arXiv preprint (not yet peer-reviewed
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📄 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

📑 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

📄 Time-Dependent Graph Generation: A Survey 🗓 Publish year: 2026 🧑‍💻Authors: Quang Nguyen, Muhammad Farhan, Asiri Wijesin
📄 Time-Dependent Graph Generation: A Survey 🗓 Publish year: 2026 🧑‍💻Authors: Quang Nguyen, Muhammad Farhan, Asiri Wijesinghe 🏢University: The Australian National University, Australia 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #graph_generation #time

📃 Graph Representation Learning in Complex Networks: Recent Advances and Open Challenges 📓 Journal: Journal of Artificial I
📃 Graph Representation Learning in Complex Networks: Recent Advances and Open Challenges 📓 Journal: Journal of Artificial Intelligence for Automation 🗓 Publish year: 2026 🧑‍💻Authors: Yue Yang, Dongxu Li, Ziwen Cui, ... 🏢University: Chinese Academy of Sciences & University of Chinese Academy of Sciences & Xinjiang Normal University, China 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #graph_representation

📑 Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey 📗 Journal: Biomolecules (I.F.=4.8) 🗓Publish year: 20
📑 Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey 📗 Journal: Biomolecules (I.F.=4.8) 🗓Publish year: 2026 🧑‍💻Authors: Chengcheng Sun, Jiayun Tian, Cheng Zhai, ... 🏢Universities: China University of Mining and Technology, China - University of Illinois at Chicago, United States 📎 Study the paper ⚡️Channel: @ComplexNetworkAnalysis #review #knowledge_graph #gnn

Repost from Bioinformatics
📃 Temporal network analysis in systems biology: concepts, inference, and validation 📕 Journal: Frontiers in Bioinformatics
📃 Temporal network analysis in systems biology: concepts, inference, and validation 📕 Journal: Frontiers in Bioinformatics (I.F.=3.6) 🗓Publish year: 2026 🧑‍💻Authors: Abir Khazaal, Fatemeh Vafaee 🏢University: University of New South Wales, Sydney, Australia 📎 Study the paper 📲Channel: @Bioinformatics #review #temporal #systems_biology