COSTNET PUBLICATIONS

1. Vinciotti, Veronica, and Ernst C. Wit. “Statistica Neerlandica special issue on Statistical Network Science.” Statistica Neerlandica 74.3 (2020): 220-221.
2. Biagini, Francesca, Göran Kauermann, and Thilo Meyer-Brandis. Network Science. Springer-Verlag, 2019.
3. Wit, Ernst C. “Big data and biostatistics: The death of the asymptotic Valhalla.” Statistics & Probability Letters 136 (2018): 30-33.

1. Castelletti, Federico, et al. “Bayesian learning of multiple directed networks from observational data.” Statistics in Medicine (2020).
2. Roverato, Alberto. Graphical models for categorical data. Cambridge University Press, 2017.
3. Kamalabad, Mahdi Shafiee, and Marco Grzegorczyk. “A New Partially Segment-Wise Coupled Piece-Wise Linear Regression Model for Statistical Network Structure Inference.” International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer, Cham, 2018.
4. Abbruzzo, Antonino, et al. “Selecting the tuning parameter in penalized Gaussian graphical models.” Statistics and Computing 29.3 (2019): 559-569.
5. Paci, Lucia, and Guido Consonni. “Structural learning of contemporaneous dependencies in graphical VAR models.” Computational Statistics & Data Analysis 144 (2020): 106880.
6. Peluso, Stefano, and Guido Consonni. “Compatible priors for model selection of high-dimensional Gaussian DAGs.” Electronic Journal of Statistics 14.2 (2020): 4110-4132.
7. Castelletti, Federico, and Guido Consonni. “Bayesian inference of causal effects from observational data in Gaussian graphical models.” Biometrics (2020).
8. Bernal, Victor, et al. “Exact hypothesis testing for shrinkage-based Gaussian graphical models.” Bioinformatics 35.23 (2019): 5011-5017.
9. Castelletti, Federico, and Guido Consonni. “Discovering causal structures in Bayesian Gaussian directed acyclic graph models.” Journal of the Royal Statistical Society: Series A (Statistics in Society) (2020).
10. Shafiee Kamalabad, Mahdi, et al. “Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices.” Bioinformatics 35.12 (2019): 2108-2117.
11. Augugliaro, Luigi, Gianluca Sottile, and Veronica Vinciotti. “The conditional censored graphical lasso estimator.” STATISTICS AND COMPUTING (2020).
12. Behrouzi, Pariya, and Ernst C. Wit. “De novo construction of polyploid linkage maps using discrete graphical models.” Bioinformatics 35.7 (2019): 1083-1093.
13. Augugliaro, Luigi, Antonino Abbruzzo, and Veronica Vinciotti. “ℓ 1-Penalized censored Gaussian graphical model.” Biostatistics 21.2 (2020): e1-e16.
14. Lupparelli, Monia, and Alessandra Mattei. “Joint and marginal causal effects for binary non-independent outcomes.” Journal of Multivariate Analysis (2020): 104609.
15. Shafiee Kamalabad, Mahdi, and Marco Grzegorczyk. “Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters.” Bioinformatics 36.4 (2020): 1198-1207.
16. Mahmoudi, Seyed Mahdi, and Ernst C. Wit. “Estimating causal effects from nonparanormal observational data.” The International Journal of Biostatistics 14.2 (2018).

1. Reinert, Gesine, and Nathan Ross. “Approximating stationary distributions of fast mixing Glauber dynamics, with applications to exponential random graphs.” The Annals of Applied Probability 29.5 (2019): 3201-3229.
2. Lebacher, Michael, and Göran Kauermann. “Regression-based Network Reconstruction with Nodal and Dyadic Covariates and Random Effects.” arXiv preprint arXiv:1903.11886 (2019).
3. Fritz, Cornelius, Michael Lebacher, and Göran Kauermann. “Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time.” Statistica Neerlandica 74.3 (2020): 275-299.
4. Gaunt, Robert E., and Neil Walton. “Stein’s method for the single server queue in heavy traffic.” Statistics & Probability Letters 156 (2020): 108566.
5. Xu, Xiaochuan, and Gesine Reinert. “Triad-based comparison and signatures of directed networks.” International Conference on Complex Networks and their Applications. Springer, Cham, 2018.
6. Kauermann, Göran, and Benjamin Sischka. “Bayesian and Spline based Approaches for (EM based) Graphon Estimation.” arXiv preprint arXiv:1903.06936 (2019).
7. De Nicola, Giacomo, Benjamin Sischka, and Göran Kauermann. “Mixture Models and Networks–Overview of Stochastic Blockmodelling.” arXiv preprint arXiv:2005.09396 (2020).
8. Cutillo, Luisa, and Mirko Signorelli. “An inferential procedure for community structure validation in networks.” arXiv preprint arXiv:1710.06611 (2017).
9. Kevork, Sevag, and Göran Kauermann. “Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects.” arXiv preprint arXiv:1911.02397 (2019).
10. Lovato, Ilenia, et al. “Model-free two-sample test for network-valued data.” Computational Statistics & Data Analysis 144 (2020): 106896.
11. Signorelli, Mirko, and Ernst C. Wit. “Model-based clustering for populations of networks.” Statistical Modelling 20.1 (2020): 9-29.
12. Cugmas, Marjan, Aleš Žiberna, and Anuška Ferligoj. “Mechanisms Generating Asymmetric Core-Cohesive Blockmodels.” Advances in Methodology & Statistics/Metodoloski zvezki 16.1 (2019).
13. Avrachenkov, Konstantin, Lasse Leskelä, and Maximilien Dreveton. “Estimation of Static Community Memberships from Temporal Network Data.” arXiv preprint arXiv:2008.04790 (2020).
14. Cutillo, Luisa, and Mirko Signorelli. “On community structure validation in real networks.” arXiv preprint arXiv:1710.06611 (2017).
15. Kolaczyk, Eric D. Topics at the Frontier of Statistics and Network Analysis:(re) visiting the Foundations. Cambridge University Press, 2017.

1. Geler, Zoltan, et al. “Time-Series Classification with Constrained DTW Distance and Inverse-Square Weighted k-NN.” 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2020.
2. Artico, I., et al. “How rare are power-law networks really?.” Proceedings of the Royal Society A 476.2241 (2020): 20190742.
3. Lucas, Maxime, Giulia Cencetti, and Federico Battiston. “Multiorder Laplacian for synchronization in higher-order networks.” Physical Review Research 2.3 (2020): 033410.
4. Haselimashhadi, Hamed. “A unified class of penalties with the capability of producing a differentiable alternative to l 1 norm penalty.” Communications in Statistics-Theory and Methods 48.22 (2019): 5530-5545.
5. Wegner, Anatol E., et al. “Identifying networks with common organizational principles.” arXiv preprint arXiv:1704.00387 (2017).
6. Geler, Zoltan, et al. “Weighted kNN and constrained elastic distances for time-series classification.” Expert Systems with Applications 162 (2020): 113829.
7. Rodríguez, Jorge P., Fakhteh Ghanbarnejad, and Víctor M. Eguíluz. “Particle velocity controls phase transitions in contagion dynamics.” Scientific reports 9.1 (2019): 1-9.
8. Millán, Ana P., et al. “Complex networks with tuneable dimensions as a universality playground.” arXiv preprint arXiv:2006.10421 (2020).
9. Milán, Janosov, et al. “Elites, communities and the limited benefits of mentorship in electronic music.” Scientific Reports (Nature Publisher Group) 10.1 (2020).
10. Acer, Seher, Oguz Selvitopi, and Cevdet Aykanat. “Addressing Volume and Latency Overheads in 1D-parallel Sparse Matrix-Vector Multiplication.” European Conference on Parallel Processing. Springer, Cham, 2017.
11. Erkuş, Ekin Can, and Vilda Purutçuoğlu. “Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD).” European Journal of Operational Research (2020).
12. Avrachenkov, Konstantin, Arun Kadavankandy, and Nelly Litvak. “Mean field analysis of personalized PageRank with implications for local graph clustering.” Journal of statistical physics 173.3-4 (2018): 895-916.
13. Šubelj, Lovro. “Convex skeletons of complex networks.” Journal of The Royal Society Interface 15.145 (2018): 20180422.
14. Lucas, Maxime, Giulia Cencetti, and Federico Battiston. “A multi-order Laplacian framework for the stability of higher-order synchronization.” arXiv preprint arXiv:2003.09734 (2020).
15. Elliott, Andrew, et al. “Core–periphery structure in directed networks.” Proceedings of the Royal Society A 476.2241 (2020): 20190783.
16. Selvitopi, Oguz, Seher Acer, and Cevdet Aykanat. “A recursive hypergraph bipartitioning framework for reducing bandwidth and latency costs simultaneously.” IEEE Transactions on Parallel and Distributed Systems 28.2 (2016): 345-358.
17. Avrachenkov, Konstantin, Patrick Brown, and Nelly Litvak. “Red Light Green Light Method for Solving Large Markov Chains.” arXiv preprint arXiv:2008.02710 (2020).
18. Selvitopi, Oguz, and Cevdet Aykanat. “Reducing latency cost in 2D sparse matrix partitioning models.” Parallel Computing 57 (2016): 1-24.

1. Naglić, Luka, and Lovro Šubelj. “War pact model of shrinking networks.” PloS one 14.10 (2019): e0223480.
2. De Benedictis, Luca, Roberto Rondinelli, and Veronica Vinciotti. “The network structure of cultural distances.” arXiv preprint arXiv:2007.02359 (2020).
3. Cugmas M, Ferligoj A, Žiberna A (2018) Generating global network structures by triad types. PLoS ONE 13(5): e0197514. www
4. Cugmas, Marjan, et al. “The social support networks of elderly people in Slovenia during the Covid-19 pandemic.” (2020).
5. De Benedictis, Luca, and Silvia Leoni. “Gender bias in the Erasmus network of universities.” Applied Network Science 5.1 (2020): 1-25.
6. Lebacher, Michael, Paul W. Thurner, and Göran Kauermann. “Censored Regression for Modelling International Small Arms Trading and its” Forensic” Use for Exploring Unreported Trades.” arXiv preprint arXiv:1902.09292 (2019).
7. Cugmas, Marjan, et al. “Symmetric core-cohesive blockmodel in preschool children’s interaction networks.” PloS one 15.1 (2020): e0226801.
8. Jarynowski, Andrzej, Alexander Semenov, and Vitaly Belik. “Protest perspective against COVID-19 risk mitigation strategies on the German Internet.”
9. Buda, Andrzej, Andrzej Jarynowski.and Katarzyna Kuzmicz. “An attempt to unified approach to the evolution of products in the entertainment industry.” E-methodology 6.6 (2019): 80-93.
10. Cencetti, Giulia, et al. “Temporal properties of higher-order interactions in social networks.” arXiv preprint arXiv:2010.03404 (2020).
11. Lozano, Vanessa, et al. “Modelling Acacia saligna invasion in a large Mediterranean island using PAB factors: A tool for implementing the European legislation on invasive species.” Ecological Indicators 116 (2020): 106516.
12. Batagelj, Vladimir, and Daria Maltseva. “Temporal bibliographic networks.” Journal of Informetrics 14.1 (2020): 101006.
13. Janosov, Milán, et al. “Elites, communities and the limited benefits of mentorship in electronic music.” Scientific reports 10.1 (2020): 1-8.
14. Jarynowski, Andrzej, and Vitaly Belik. “Choroby przenoszone drogą płciową w dobie Internetu i e-zdrowia: kalkulatory ryzyka.” (2018).
15. Richter, Francisco, et al. “Introducing a general class of species diversification models for phylogenetic trees.” Statistica Neerlandica (2020).
16. Ranciati, Saverio, Veronica Vinciotti, and Ernst C. Wit. “Identifying overlapping terrorist cells from the Noordin Top actor–event network.” Annals of Applied Statistics 14.3 (2020): 1516-1534.
17. Lebacher, Michael, Paul W. Thurner, and Göran Kauermann. “Exploring dependence structures in the international arms trade network: a network autocorrelation approach.” Statistical Modelling 20.2 (2020): 195-218.
18. Bauer, Verena, Dietmar Harhoff, and Göran Kauermann. “A smooth dynamic network model for patent collaboration data.” arXiv preprint arXiv:1909.00736 (2019).
19. Jarynowski, Andrzej, Michał B. Paradowski, and Andrzej Buda. “Modelling communities and populations: an introduction to computational social science.” (2019).
20. Šubelj, Lovro, et al. “Convexity in scientific collaboration networks.” Journal of Informetrics 13.1 (2019): 10-31.
21. Signorelli, Mirko, and Ernst C. Wit. “A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament.” arXiv preprint arXiv:1607.08743 (2016).
22. Užupytė, Rūta, and Ernst C. Wit. “Test for triadic closure and triadic protection in temporal relational event data.” Social Network Analysis and Mining 10.1 (2020): 1-12.
23. Schneble, Marc, and Göran Kauermann. “Estimation of Latent Network Flows in Bike-Sharing Systems.” arXiv preprint arXiv:2001.08146 (2020).
24. Wegner, Anatol E., et al. “Identifying networks with common organizational principles.” Journal of Complex Networks 6.6 (2018): 887-913.
25. Paradowski, Michał B., et al. “Peer interactions and second language learning: The contributions of social network analysis in immersion/study abroad vs at-home environments-preprint.” (2020).
26. Maltseva, Daria, and Vladimir Batagelj. “Towards a systematic description of the field using keywords analysis: main topics in social networks.” Scientometrics (2020): 1-26.
27. Šubelj, Lovro, et al. “Intermediacy of publications.” Royal Society Open Science 7.1 (2020): 190207.
28. Paradowski, Michał B., et al. “Peer interactions and second language learning: The contributions of Social Network Analysis in Immersion/Study Abroad vs Stay-at-Home environments.” (2020).
29. Maltseva, Daria, and Vladimir Batagelj. “iMetrics: the development of the discipline with many names.” Scientometrics 125.1 (2020): 313-359.
30. Perczel, Júlia. “Is Structure Context or Content? A DataDriven Method of Comparing Museum Collections” Život umjetnosti: časopis o modernoj i suvremenoj umjetnosti i arhitekturi 105.2 (2019): 76-109.

1. Fontana, Magda, et al. “New and atypical combinations: An assessment of novelty and interdisciplinarity.” Research Policy 49.7 (2020): 104063.
2. Lebacher, Michael, et al. “In search of lost edges: a case study on reconstructing financial networks.” arXiv preprint arXiv:1909.01274 (2019).
3. Khraisha, Tamer, and Rosario N. Mantegna. “Network structure and optimal technological innovation.” Journal of Complex Networks 8.1 (2020): cnz020.
4. Fontana, Magda, et al. A bridge over troubled water: interdisciplinarity, novelty, and impact. No. dipe0002. Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE), 2018.

1. Engebretsen, Solveig. “Contributions to network science in public health.” (2019).
2. Bozhilova, Lyuba V., et al. “COGENT: evaluating the consistency of gene co-expression networks.” BioRxiv (2020).
3. Pardo-Diaz, Javier, et al. “Robust gene coexpression networks using signed distance correlation.” BioRxiv (2020).
4. Ağraz, Melih, and Vilda Purutçuoğlu. “Long-tailed graphical model and frequentist inference of the model parameters for biological networks.” Journal of Statistical Computation and Simulation 90.9 (2020): 1591-1605.
5. Paci, L., and G. Consonni. “Graphical model selection for air quality time series.” GRASPA 2019 Conference, Pescara, 15-16 July 2019. Università degli studi di Bergamo, 2019.
6. Babac, Marina Bagić, and Vedran Mornar. “Resetting the Initial Conditions for Calculating Epidemic Spread: COVID-19 Outbreak in Italy.” IEEE Access 8 (2020): 148021-148030.
7. Bülbül, G. B., V. Purutçuoğlu, and E. Purutçuoğlu. “Novel model selection criteria on sparse biological networks.” International Journal of Environmental Science and Technology (2019): 1-6.
8. Rasero, Javier, et al. “The usefulness of Bayesian graphical modelling with neuropsychological data.” (2019).
9. Cougoul, Arnaud, Xavier Bailly, and Ernst C. Wit. “MAGMA: inference of sparse microbial association networks.” BioRxiv (2019): 538579.
10. Ağraz, Melih, and Vilda Purutçuoğlu. “Extended lasso-type MARS (LMARS) model in the description of biological network.” Journal of Statistical Computation and Simulation 89.1 (2019): 1-14.
11. Pinho, André, et al. “Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection.” Applied Soft Computing 83 (2019): 105568.
12. Britton, Tom, and Gianpaolo Scalia Tomba. “Estimation in emerging epidemics: Biases and remedies.” Journal of the Royal Society Interface 16.150 (2019): 20180670.
13. Petek, Marko, et al. “Cultivar-specific transcriptome and pan-transcriptome reconstruction of tetraploid potato.” bioRxiv (2020): 845818.
14. Fritz, Cornelius, and Göran Kauermann. “On the interplay of regional mobility, social connectedness, and the spread of covid-19 in germany.” arXiv preprint arXiv:2008.03013 (2020).
15. Berta, Paolo, Veronica Vinciotti, and Francesco Moscone. “Does hospital cooperation increase the quality of healthcare?.” arXiv preprint arXiv:1911.04168 (2019).
16. Dar, Elif Doğan, Vilda Purutçuoğlu, and Eda Purutçuoğlu. “Detection of HIV-1 Protease Cleavage Sites via Hidden Markov Model and Physicochemical Properties of Amino Acids.” Numerical Solutions of Realistic Nonlinear Phenomena. Springer, Cham, 2020. 171-193.
17. Souza-Pereira, Leonice, et al. “Clinical Decision Support Systems for Chronic Diseases: A Systematic Literature Review.” Computer Methods and Programs in Biomedicine (2020): 105565.
18. Wit, Ernst C., et al. “Sparse relative risk regression models.” Biostatistics 21.2 (2020): e131-e147.
19. Signorelli, Mirko, Veronica Vinciotti, and Ernst C. Wit. “NEAT: an efficient network enrichment analysis test.” BMC bioinformatics 17.1 (2016): 352.
20. Pellin, Danilo, et al. “Penalized inference of the hematopoietic cell differentiation network via high-dimensional clonal tracking.” Applied Network Science 4.1 (2019): 115.
21. Arani, Babak MS, et al. “Stability estimation of autoregulated genes under Michaelis-Menten-type kinetics.” Physical Review E 97.6 (2018): 062407.
22. Letina, Srebrenka, et al. “Expanding network analysis tools in psychological networks: Minimal spanning trees, participation coefficients, and motif analysis applied to a network of 26 psychological attributes.” Complexity 2019 (2019).
23. Acer, Seher, Oguz Selvitopi, and Cevdet Aykanat. “Optimizing nonzero-based sparse matrix partitioning models via reducing latency.” Journal of Parallel and Distributed Computing 122 (2018): 145-158.
24. Garcia, Nuno M., et al. “Keyed user datagram protocol: concepts and operation of an almost reliable connectionless transport protocol.” IEEE Access 7 (2019): 18951-18963.

1. Martens, Erik Andreas, and Konstantin Klemm. “Cyclic structure induced by load fluctuations in adaptive transportation networks.” Progress in Industrial Mathematics at ECMI 2018. Springer, Cham, 2019. 147-155.
2. Martens, Erik A., and Konstantin Klemm. “Transitions from trees to cycles in adaptive flow networks.” Frontiers in Physics 5 (2017): 62.
3. Rodríguez-García, Jorge Pablo, Fakhteh Ghanbarnejad, and Víctor M. Eguíluz. “Particle velocity controls phase transitions in contagion dynamics.” (2019).

Publications; CostNet