Gupta, Prateek, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, et al. ‘COVI-AgentSim: An Agent-Based Model for Evaluating Methods of Digital Contact Tracing’. ArXiv:2010.16004 [Cs], 29 October 2020. http://arxiv.org/abs/2010.16004.

McCabe, Stefan, Leo Torres, Timothy LaRock, Syed Arefinul Haque, Chia-Hung Yang, Harrison Hartle, and Brennan Klein. ‘Netrd: A Library for Network Reconstruction and Graph Distances’. ArXiv:2010.16019 [Physics], 29 October 2020. http://arxiv.org/abs/2010.16019.

Mazumdar, S., & Thakker, D. (2020). Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet, 12(12), 210. https://doi.org/10.3390/fi12120210

https://www.mdpi.com/1999-5903/12/12/210

2020 Conference on Computational Sociology | IRiSS. (n.d.). Retrieved 30 September 2020, from https://iriss.stanford.edu/css/conferences/2020-conference-computational-sociology

Romanini, Daniele, Sune Lehmann, and Mikko Kivelä. ‘Privacy and Uniqueness of Neighborhoods in Social Networks’. ArXiv:2009.09973 [Physics], 21 September 2020. http://arxiv.org/abs/2009.09973.

Ali, A. (2020, August 28). Visualizing the Social Media Universe in 2020. Visual Capitalist. https://www.visualcapitalist.com/visualizing-the-social-media-universe-in-2020/

Holtz, D., Zhao, M., Benzell, S. G., Cao, C. Y., Rahimian, M. A., Yang, J., Allen, J., Collis, A., Moehring, A., Sowrirajan, T., Ghosh, D., Zhang, Y., Dhillon, P. S., Nicolaides, C., Eckles, D., & Aral, S. (2020). Interdependence and the cost of uncoordinated responses to COVID-19. Proceedings of the National Academy of Sciences, 117(33), 19837–19843. https://doi.org/10.1073/pnas.2009522117

Young, J.-G., Cantwell, G. T., & Newman, M. E. J. (2020). Robust Bayesian inference of network structure from unreliable data. ArXiv:2008.03334 [Physics, Stat]. http://arxiv.org/abs/2008.03334

Aleta, A., Arruda, G. F. de, & Moreno, Y. (2020). Data-driven contact structures: From homogeneous mixing to multilayer networks. PLOS Computational Biology, 16(7), e1008035. https://doi.org/10.1371/journal.pcbi.1008035

Supporting Open Science Data Curation, Preservation, and Access by Libraries. (2020, June 25). https://www.youtube.com/watch?v=SbmGWHpzAHs&feature=youtu.be

Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063. https://doi.org/10.1016/j.respol.2020.104063

Gupta, H., & Porter, M. A. (2020). Mixed Logit Models and Network Formation. ArXiv:2006.16516 [Physics, Stat]. http://arxiv.org/abs/2006.16516

Lovato, J., Allard, A., Harp, R., & Hébert-Dufresne, L. (2020). Distributed consent and its impact on privacy and observability in social networks. ArXiv:2006.16140 [Physics]. http://arxiv.org/abs/2006.16140

Welcome! You are invited to join a webinar: Supporting Open Science Data Curation, Preservation, and Access by Libraries. After registering, you will receive a confirmation email about joining the webinar. (n.d.). Zoom Video. Retrieved June 28, 2020, from https://zoom.us/webinar/register/2615905946283/WN_W6dYUXQFTqGQjGAZPRB74w

Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & van Mieghem, P. (2020). BECON methodology [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/53ey9

UKRN position on covid 19 research. (2020 May 01). School of Psychological Science | University of Bristol. http://www.bristol.ac.uk/psychology/research/ukrn/news/2020/ukrn-position-on-covid-19-research.html

Murphy, C., Laurence, E., & Allard, A. (2020). Deep learning of stochastic contagion dynamics on complex networks. ArXiv:2006.05410 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.05410

Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo Chambers on Social Media: A comparative analysis. ArXiv:2004.09603 [Physics]. http://arxiv.org/abs/2004.09603

Our Work on COVID-19. (n.d.). Facebook Data for Good. Retrieved April 20, 2020, from https://dataforgood.fb.com/docs/covid19/

Eroglu, D. (2020). Revealing Dynamics, Communities, and Criticality from Data. Physical Review X, 10(2). https://doi.org/10.1103/PhysRevX.10.021047

Cantwell, G. T., Liu, Y., Maier, B. F., Schwarze, A. C., Serván, C. A., Snyder, J., & St-Onge, G. (2020). Thresholding normally distributed data creates complex networks. Physical Review E, 101(6), 062302. https://doi.org/10.1103/PhysRevE.101.062302

Rosenblatt, S. F., Smith, J. A., Gauthier, G. R., & Hébert-Dufresne, L. (2020). Immunization Strategies in Networks with Missing Data. ArXiv:2005.07632 [Physics, q-Bio]. http://arxiv.org/abs/2005.07632

Zdeborová, L. (2020). Understanding deep learning is also a job for physicists. Nature Physics, 1–3. https://doi.org/10.1038/s41567-020-0929-2

Rybniker, J., & Fätkenheuer, G. (2020). Importance of precise data on SARS-CoV-2 transmission dynamics control. The Lancet Infectious Diseases, S1473309920303595. https://doi.org/10.1016/S1473-3099(20)30359-5

Dahl Fitjar, R. (2020, May 9). The density and connectedness of cities now appear as weaknesses. LSE Business Review. https://blogs.lse.ac.uk/businessreview/2020/05/09/the-density-and-connectedness-of-cities-now-appear-as-weaknesses/

Vespignani, A., Tian, H., Dye, C. et al. Modelling COVID-19. Nat Rev Phys (2020). https://doi.org/10.1038/s42254-020-0178-4

Qian, Y., Expert, P., Panzarasa, P., & Barahona, M. (2020). Geometric graphs from data to aid classification tasks with graph convolutional networks. ArXiv:2005.04081 [Physics, Stat]. http://arxiv.org/abs/2005.04081

Countries are using apps and data networks to keep tabs on the pandemic. (2020 March 26). The Economist. https://www.economist.com/briefing/2020/03/26/countries-are-using-apps-and-data-networks-to-keep-tabs-on-the-pandemic?fsrc=newsletter&utm_campaign=the-economist-today&utm_medium=newsletter&utm_source=salesforce-marketing-cloud&utm_term=2020-05-07&utm_content=article-link-1

Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). COVID-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing. Journal of Risk Research, 0(0), 1–4. https://doi.org/10.1080/13669877.2020.1756381

Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., & Donges, J. F. (2020). Dynamics of tipping cascades on complex networks. Physical Review E, 101(4), 042311. https://doi.org/10.1103/PhysRevE.101.042311

Jiang, C., Gao, J., & Magdon-Ismail, M. (2020). Inferring Degrees from Incomplete Networks and Nonlinear Dynamics. ArXiv:2004.10546 [Physics]. http://arxiv.org/abs/2004.10546

Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19

Marc Rysman - BU

Neat use of TAGS Explorer. Did do so, to interesting effects. Regular livetweet events may be more interesting. And there’s something to be said about NodeXL and COSMOS.

Even those who adopt a taylorist model to education may understand that “it takes money to save money”.