π Network model selection: A review of methods
πPublisher: Springer Nature β SpringerBriefs
π Publication year: 2026
π§βπ»Author: Zoran LevnajiΔ
π’Affiliation: Faculty of Information Studies in Novo mesto, Slovenia
π 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.
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #analysis #model
πPublisher: Springer Nature β SpringerBriefs
π Publication year: 2026
π§βπ»Author: Zoran LevnajiΔ
π’Affiliation: Faculty of Information Studies in Novo mesto, Slovenia
π 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.
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #analysis #model
π2
π Multilayer public transport networks
π Publish year: 2026
π§βπ» Authors: Tina Ε filigoj, Renzo Massobrio, Oded Cats
π’ Universities: University of Ljubljana, Slovenia β University of Antwerp, Belgium β Delft University of Technology, The Netherlands
π 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.
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #multilayer #transportation
π Publish year: 2026
π§βπ» Authors: Tina Ε filigoj, Renzo Massobrio, Oded Cats
π’ Universities: University of Ljubljana, Slovenia β University of Antwerp, Belgium β Delft University of Technology, The Netherlands
π 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.
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #multilayer #transportation
π1
π Explainable AI for Graph-Based Learning: A Survey Beyond Graph Neural Networks
π Publish year: 2026
π§βπ»Authors: Margarita BugueΓ±o, Russa Biswas, Gerard de Melo
π’Universities: Hasso Plattner Institute (HPI) / University of Potsdam, Germany - Aalborg University, Denmark
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #explainable #ai #gnn
π Publish year: 2026
π§βπ»Authors: Margarita BugueΓ±o, Russa Biswas, Gerard de Melo
π’Universities: Hasso Plattner Institute (HPI) / University of Potsdam, Germany - Aalborg University, Denmark
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #explainable #ai #gnn
π1
πΉ Graph Reconstruction
π Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #graph #reconstruction
π Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #graph #reconstruction
π1
π From prior knowledge to data-informed models: a review of Boolean network inference
π Journal: Briefings in Bioinformatics (I.F.=7.3)
π Publish year: 2026
π§βπ» Authors: Pierre Klemmer, Ahmed Abdelmonem Hemedan, Reinhard Schneider, Marek Ostaszewski
π’ Universities: University of Luxembourg & Centre for Systems Biomedicine (LCSB) & Luxembourg Institute of Health (LIH), Luxembourg
π Study the paper: https://doi.org/10.1093/bib/bbag499
β‘οΈChannel: @ComplexNetworkAnalysis
#review #systemsbiology #boolean_network
π Journal: Briefings in Bioinformatics (I.F.=7.3)
π Publish year: 2026
π§βπ» Authors: Pierre Klemmer, Ahmed Abdelmonem Hemedan, Reinhard Schneider, Marek Ostaszewski
π’ Universities: University of Luxembourg & Centre for Systems Biomedicine (LCSB) & Luxembourg Institute of Health (LIH), Luxembourg
π Study the paper: https://doi.org/10.1093/bib/bbag499
β‘οΈChannel: @ComplexNetworkAnalysis
#review #systemsbiology #boolean_network
OUP Academic
From prior knowledge to data-informed models: a review of Boolean network inference
Abstract. Molecular mechanisms are highly complex and involve numerous components in non-linear interactions, imposing challenges in analysis and understan
β€1π1
π A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods
π Journal: ACM Computing Surveys (π₯I.F.=30.4)
π Publish year: 2026
π§βπ» Authors: Amir Sheikhahmadi, Laleh Tafakori, Mahdi Jalili
π’ University: RMIT University, Melbourne, Australia
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #influential_node
π Journal: ACM Computing Surveys (π₯I.F.=30.4)
π Publish year: 2026
π§βπ» Authors: Amir Sheikhahmadi, Laleh Tafakori, Mahdi Jalili
π’ University: RMIT University, Melbourne, Australia
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #influential_node
π₯2β€1π1
π A Survey on GNN-Based Link Prediction: Techniques, Applications, and Challenges
π Journal: WIREs Data Mining and Knowledge Discovery (I.F.=15)
π Publish year: 2026
π§βπ»Authors: Chengcheng Sun, Yajie Song, Cheng Zhai, Jiayun Tian, Jia Yang, Xiaobin Rui, Jian Zhang, Zhixiao Wang, Philip S. Yu
π’Universities: China University of Mining and Technology, China β University of Illinois Chicago, USA
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #link_prediction #gnn
π Journal: WIREs Data Mining and Knowledge Discovery (I.F.=15)
π Publish year: 2026
π§βπ»Authors: Chengcheng Sun, Yajie Song, Cheng Zhai, Jiayun Tian, Jia Yang, Xiaobin Rui, Jian Zhang, Zhixiao Wang, Philip S. Yu
π’Universities: China University of Mining and Technology, China β University of Illinois Chicago, USA
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #link_prediction #gnn
β€2
Forwarded from Bioinformatics
π Graph designs for deep learningβbased multi-omics integration
π Journal: Briefings in Bioinformatics (I.F.=7.3)
π Publish year: 2026
π§βπ»Authors: Muhtasim Noor Alif, Khandakar Tanvir Ahmed, Sudipto Baul, Wei Zhang
π’University: University of Central Florida, USA
π Study the paper
π²Channel: @Bioinformatics
#review #multiomics #graph #gnn #deeplearning
π Journal: Briefings in Bioinformatics (I.F.=7.3)
π Publish year: 2026
π§βπ»Authors: Muhtasim Noor Alif, Khandakar Tanvir Ahmed, Sudipto Baul, Wei Zhang
π’University: University of Central Florida, USA
π Study the paper
π²Channel: @Bioinformatics
#review #multiomics #graph #gnn #deeplearning
π1
Forwarded from Bioinformatics
π A Review and Experimental Analysis of Supervised Learning Systems and Methods for ProteinβProtein Interaction Detection
π Journal: International Journal of Molecular Sciences (I.F. = 5.6)
π Publish year: 2026
π§βπ» Author: Kamal Taha
π’ Affiliations: Khalifa University, United Arab Emirates
π Study the paper
π² Channel: @Bioinformatics
#review #machinelearning #proteomics #PPI
π Journal: International Journal of Molecular Sciences (I.F. = 5.6)
π Publish year: 2026
π§βπ» Author: Kamal Taha
π’ Affiliations: Khalifa University, United Arab Emirates
π Study the paper
π² Channel: @Bioinformatics
#review #machinelearning #proteomics #PPI
β€2
π From graphs to qubits: a critical review of quantum graph neural networks
π Journal: Neural Computing and Applications (I.F.=4.5)
π Publish year: 2026
π§βπ» Authors: Andrea Ceschini, Francesco Mauro, Francesca De Falco, Alessandro Sebastianelli, ...
π’Universities: University of Rome βLa Sapienzaβ & University of Sannio & European Space Agency, Italy
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #qubits #gnn
π Journal: Neural Computing and Applications (I.F.=4.5)
π Publish year: 2026
π§βπ» Authors: Andrea Ceschini, Francesco Mauro, Francesca De Falco, Alessandro Sebastianelli, ...
π’Universities: University of Rome βLa Sapienzaβ & University of Sannio & European Space Agency, Italy
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #qubits #gnn
π1