Peer-reviewed papers

  1. Andriollo, E., Secco, L., Caimo, A., and Pisani, E. (2023) “Probabilistic network analysis of social-ecological relationships emerging from EU LIFE projects for nature and biodiversity: An application of ERGM models in the case study of the Veneto region (Italy).” Environmental Science & Policy, 148, 103550. [Open access]
  2. Caimo, A. and Gollini, I. (2023) “Recent Advances in Exponential Random Graph Modelling.” Mathematical Proceedings of the Royal Irish Academy. 123A(1), 1–12. [doi:10.1353/mpr.2023.0000]
  3. Caimo, A., Bouranis, L., Krause, R., and Friel, N. (2022) “Statistical Network Analysis with Bergm.” Journal of Statistical Software, 104(1), 1–23. [10.18637/jss.v104.i01]
  4. Leva, M. C., Demichela, M., Comberti, L., and Caimo, A. (2022). “Human Performance in Manufacturing Tasks: Optimization and Assessment of Required Workload and Capabilities.” Safety Science, 154, 105838.
  5. Arrizza, A. and Caimo, A. (2021). “Bayesian Dynamic Network Actor Models with Application to South Korean COVID-19 Patient Movement Data.” Statistical Methods & Applications, 30(5), 1465 – 1483. [Open access]
  6. Andriollo, E., Caimo, A., Secco, L., and Pisani, E. (2021). “Collaborations in Environmental Initiatives for an Effective Adaptive Governance of Social–Ecological Systems: What Existing Literature Suggests.” Sustainability, 13(15), 8276. [Open access]
  7. Gollini, I., Caimo, A., and Campana, P. (2020). “Modelling Interactions among Offenders: A Latent Space Approach for Interdependent Ego-networks.” Social Networks, 63, 134 – 149. [Open access]
  8. Akbaritabar, A., Traag, V. A., Caimo, A., and Squazzoni, F. (2020), “Italian Sociologists: A Community of Disconnected Groups.” Scientometrics, 124, 2361– 2382. [Open access]
  9. Caimo, A. and Gollini, I. (2020), “A Multilayer Exponential Random Graph Modelling Approach for Weighted Networks.” Computational Statistics and Data Analysis, 142, 106825. [arXiv:1811.07025]
  10. Tasselli, S. and Caimo, A. (2019), “Does It Take Three to Dance the Tango? Organizational Design, Triadic Structures and Boundary Spanning across Subunits.” Social Networks, 59, 10 – 22.
  11. Caimo, A. and Gollini, I. (2019), “Modelling Weighted Signed Networks.” Proceedings of the Conference of the Italian Statistical Society 2019, 111 – 118. [Book of Papers]
  12. Balest, J., Secco, L., Pisani, E., and Caimo, A. (2019), “Sustainable Energy Governance in South Tyrol (Italy): A Probabilistic Bipartite Network Model.” Journal of Cleaner Production, 221, 854 – 862. [Open access]
  13. Krause, R. W. and Caimo, A. (2019), “Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models.” International Workshop on Complex Networks, 221, 63 – 72. Springer.
  14. Leva, M. C., Caimo, A., Duane, R., Demichela, M. and Comberti L. (2018), “Task Complexity, and Operators’ Capabilities as Predictor of Human Error: Modeling Framework and an Example of Application”, In Safety and Reliability–Safe Societies in a Changing World, 493 – 499. CRC Press.
  15. Agasarova, A., Harnett, C., Mulligan, N., Majeed, M. S., Caimo, A., and Tamagno, G. (2018), “Management and Follow-up of Patients with a Bronchial Neuroendocrine Tumor in the Last Twenty Years in Ireland: Expected Inconsistencies and Unexpected Discoveries.” International Journal of Endocrinology.
  16. Tamagno, G., Scherer, V., Caimo, A., Bergmann, S. R., and Kann, P. H. (2018), “Endoscopic Ultrasound Features of Multiple Endocrine Neoplasia Type 1-Related versus Sporadic Pancreatic Neuroendocrine Tumors: A Single-Center Retrospective Study,” Digestion, 98(2), 112 – 118.
  17. Caimo, A., Pallotti, F. and Lomi, A. (2017), “Bayesian Exponential Random Graph Modelling of Interhospital Patient Referral Networks,” Statistics in Medicine, 36(18), 2902 – 2920.
  18. Sinke, M., Dijkhuizen, R., Caimo, A., Stam, C., and Otte, W. (2016), “Bayesian Exponential Random Graph Modeling of Whole-brain Structural Networks across Lifespan,” NeuroImage, 135, 79 – 91.
  19. Thiemichen, S., Friel, N., Caimo, A. and Kauermann, G. (2016), “Bayesian Exponential Random Graph Models with Nodal Random Effects,” Social Networks, 46, 11 – 28. [arXiv:1407.6895]
  20. Koskinen, J., Caimo, A. and Lomi, A. (2015), “Simultaneous Modelling of Initial Conditions and Time Heterogeneity in Dynamic Networks: An Application to Foreign Direct Investments,” Network Science, 3(1), 58 – 77.
  21. Caimo, A. and Mira, A. (2015), “Efficient Computational Strategies for Doubly Intractable Problems with Applications to Bayesian Social Networks,” Statistics and Computing, 25(1), 113 – 125. [arXiv:1403.4402]
  22. Caimo, A. and Lomi, A. (2015), “Knowledge Sharing in Organisations: a Bayesian Analysis of the Role of Reciprocity and Formal Structure,” Journal of Management (Special Issue on Bayesian Statistics in Management Research), 41(2), 665 – 691.
  23. Caimo, A. and Friel, N. (2014), “Bergm: Bayesian Exponential Random Graphs in R,” Journal of Statistical Software, 61(2), 1 – 25. [jstatsoft.org/v61/i02]
  24. Caimo, A. and Mira, A. (2014), “Delayed Rejection Algorithm to Estimate Bayesian Social Networks,” QdS - Journal of Methodological and Applied Statistics, 16(1), 33 – 44.
  25. Caimo, A. and Friel, N. (2013), “Bayesian Model Selection for Exponential Random Graph Models,” Social Networks, 35(1), 11. [arXiv:1201.2337]
  26. Caimo, A. and Friel, N. (2011), “Bayesian Inference for Exponential Random Graph Models,” Social Networks, 33(1), 41 – 55. [arXiv:1007.5192]

Invited book chapters

  1. Caimo, A. (2016), “Exponential Random Graph Modelling of Static and Dynamic Social Networks.” In: Adams, N. M., Heard, N. A. (eds). Dynamic Networks and Cyber-Security, London: Imperial College Press.
  2. Caimo, A. and Gollini, I. (2016), “Bayesian Computational Algorithms for Social Network Analysis.” In: Dehmer, M., Shi, Y., Emmert-Streib F. (eds). Computational Network Analysis with R: Applications in Biology, Medicine, and Chemistry, New York: Wiley. [arxiv:1504.03152]
  3. Caimo, A. and Friel, N. (2014), “Actor-based Models for Longitudinal Networks.” In: Alhajj, R., Rokne J. (eds). Encyclopedia of Social Network Analysis and Mining, 9 – 18. New York: Springer.