- May 2021
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psyarxiv.com psyarxiv.com
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Gallacher, J., & Bright, J. (2021). Hate Contagion: Measuring the spread and trajectory of hate on social media. PsyArXiv. https://doi.org/10.31234/osf.io/b9qhd
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journals.plos.org journals.plos.org
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Piotrowska, M. J., Sakowski, K., Karch, A., Tahir, H., Horn, J., Kretzschmar, M. E., & Mikolajczyk, R. T. (2020). Modelling pathogen spread in a healthcare network: Indirect patient movements. PLOS Computational Biology, 16(11), e1008442. https://doi.org/10.1371/journal.pcbi.1008442
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- Apr 2021
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www.sciencedirect.com www.sciencedirect.com
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Lutkenhaus, R. O., Jansz, J., & Bouman, M. P. A. (2019). Mapping the Dutch vaccination debate on Twitter: Identifying communities, narratives, and interactions. Vaccine: X, 1. https://doi.org/10.1016/j.jvacx.2019.100019
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www.jmir.org www.jmir.org
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Grant, L., Hausman, B. L., Cashion, M., Lucchesi, N., Patel, K., & Roberts, J. (2015). Vaccination Persuasion Online: A Qualitative Study of Two Provaccine and Two Vaccine-Skeptical Websites. Journal of Medical Internet Research, 17(5), e4153. https://doi.org/10.2196/jmir.4153
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www.tandfonline.com www.tandfonline.com
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Smith, N., & Graham, T. (2019). Mapping the anti-vaccination movement on Facebook. Information, Communication & Society, 22(9), 1310–1327. https://doi.org/10.1080/1369118X.2017.1418406
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www.kickstarter.com www.kickstarter.com
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We, at Cubiko Games, would love for Foundation to reach as many people as possible because it’s such a great game. We hope that the ‘stretch goals’, ’2 x reward‘ tiers and ’voucher codes’ will encourage people to back and share the campaign so that it reaches its full potential. Then, hopefully, with more backers comes more exposure which, in turn, leads to the ultimate goal..... Foundation gets signed by a leading game manufacturer.
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www.kickstarter.com www.kickstarter.com
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Only the Starter Kit is available in this reboot. The Starter Kit is FREE, in order to distribute it as widely as possible. This goal of this Kickstarter campaign is to introduce Clash of Deck to the whole word and to bring a community together around the game. If the Kickstarter campaign succeeds, we will then have the necessary dynamic to publish additional paid content on a regular basis, to enrich the game with: stand-alone expansions, additional modules, alternative game modes..
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www.plixer.com www.plixer.com
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- Mar 2021
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Karimi, Fariba, and Petter Holme. ‘A Temporal Network Version of Watts’s Cascade Model’. ArXiv:2103.13604 [Physics], 25 March 2021. http://arxiv.org/abs/2103.13604.
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arxiv.org arxiv.org
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Holme, Petter, and Jari Saramäki. ‘Temporal Networks as a Modeling Framework’. ArXiv:2103.13586 [Physics], 24 March 2021. http://arxiv.org/abs/2103.13586.
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en.wikipedia.org en.wikipedia.org
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A semantic similarity network (SSN) is a special form of semantic network.[1] designed to represent concepts and their semantic similarity. Its main contribution is reducing the complexity of calculating semantic distances.
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Devriendt, K., Martin-Gutierrez, S., & Lambiotte, R. (2020). Variance and covariance of distributions on graphs. ArXiv:2008.09155 [Physics, Stat]. http://arxiv.org/abs/2008.09155
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arxiv.org arxiv.org
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Di Lauro, F., Berthouze, L., Dorey, M. D., Miller, J. C., & Kiss, I. Z. (2020). The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: A mean-field model perspective. ArXiv:2007.06975 [Physics, q-Bio]. http://arxiv.org/abs/2007.06975
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pediatrics.aappublications.org pediatrics.aappublications.org
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Brunson, E. K. (2013). The Impact of Social Networks on Parents’ Vaccination Decisions. Pediatrics, 131(5), e1397–e1404. https://doi.org/10.1542/peds.2012-2452
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Wang, Xiangrong, Alejandro Tejedor, Yi Wang, and Yamir Moreno. ‘Unique Superdiffusion Induced by Directionality in Multiplex Networks’. ArXiv:2011.00991 [Physics], 2 November 2020. http://arxiv.org/abs/2011.00991.
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arxiv.org arxiv.org
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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.
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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.
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arxiv.org arxiv.org
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Hota, Ashish R., Tanya Sneh, and Kavish Gupta. ‘Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks’. ArXiv:2011.00445 [Physics], 1 November 2020. http://arxiv.org/abs/2011.00445.
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psyarxiv.com psyarxiv.com
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Heeren, A., HANSEEUW, B., Cougnon, L., & Lits, G. (2021, March 11). Excessive Worrying as the Driving Force of Anxiety During the First COVID-19 Lockdown-Phase in Belgium. https://doi.org/10.31234/osf.io/b34aj
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arxiv.org arxiv.org
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Barrat, A., de Arruda, G. F., Iacopini, I., & Moreno, Y. (2021). Social contagion on higher-order structures. ArXiv:2103.03709 [Physics]. http://arxiv.org/abs/2103.03709
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www.bmj.com www.bmj.com
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Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ, 337, a2338. https://doi.org/10.1136/bmj.a2338
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- Feb 2021
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www.coursera.org www.coursera.org
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Integrity is distributed among nodes, not vested in a single member.
Network integrity
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science.sciencemag.org science.sciencemag.org
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Gordon, D. E., Hiatt, J., Bouhaddou, M., Rezelj, V. V., Ulferts, S., Braberg, H., Jureka, A. S., Obernier, K., Guo, J. Z., Batra, J., Kaake, R. M., Weckstein, A. R., Owens, T. W., Gupta, M., Pourmal, S., Titus, E. W., Cakir, M., Soucheray, M., McGregor, M., … Krogan, N. J. (2020). Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Science, 370(6521). https://doi.org/10.1126/science.abe9403
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link.aps.org link.aps.org
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Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F. (2020). Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases. Physical Review X, 10(4), 041042. https://doi.org/10.1103/PhysRevX.10.041042
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www.schneems.com www.schneems.com
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The alternative was to have multiple scripts or stylesheet links on one page, which would trigger multiple HTTP requests. Multiple requests mean multiple connection handshakes for each link “hey, I want some data”, “okay, I have the data”, “alright I heard that you have the data, give it to me” (SYN, ACK, SYNACK). Even once the connection is created there is a feature of TCP called TCP slow start that will throttle the speed of the data being sent at the beginning of a request to a slower speed than the end of the request. All of this means transferring one large request is faster than transferring the same data split up into several smaller requests.
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bylinetimes.com bylinetimes.com
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Times info@bylinetimes.com (https://bylinetimes.com/), Byline. „Cambridge Analytica Psychologist Advising Global COVID-19 Disinformation Network Linked to Nigel Farage and Conservative Party“. Byline Times, 2. Februar 2021. https://bylinetimes.com/2021/02/02/cambridge-analytica-psychologist-advising-global-covid-19-disinformation-network-linked-to-nigel-farage-and-conservative-party/.
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Hickok, A., Kureh, Y., Brooks, H. Z., Feng, M., & Porter, M. A. (2021). A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs. ArXiv:2102.06825 [Nlin, Physics:Physics]. http://arxiv.org/abs/2102.06825
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cdixon.org cdixon.org
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A popular strategy for bootstrapping networks is what I like to call “come for the tool, stay for the network.” The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.
This is an interesting and useful strategy. I've heard the idea several times before.
I'm curious if this is the oldest version of it? I have to imagine that there are earlier versions of it dating back to 2011 or 2012 if not earlier.
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www.wired.com www.wired.com
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Ogbunu, B. C. (2020, October 27). The Science That Spans #MeToo, Memes, and Covid-19. Wired. https://www.wired.com/story/the-science-that-spans-metoo-memes-and-covid-19/
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parsejournal.com parsejournal.comPARSE1
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ost-humanist perspective that foregrounds the apparatuses within which possibilities for action and judgement take shape, and confront visitors with the complex ways in which they are part of these systems and networks. How to be a responsible node in an Actor-Network?
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www.quora.com www.quora.com
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why a company like Facebook invests so much research and engineering into the network performance of things as seemingly trivial as notifications.
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journals.plos.org journals.plos.org
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Nande A, Adlam B, Sheen J, Levy MZ, Hill AL (2021) Dynamics of COVID-19 under social distancing measures are driven by transmission network structure. PLoS Comput Biol 17(2): e1008684. https://doi.org/10.1371/journal.pcbi.1008684
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10.11.66.200 10.11.66.200
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MasterCard
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medium.com medium.com
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I’d notice the network requests going out!Where would you notice them? My code won’t send anything when the DevTools are open (yes even if un-docked).I call this the Heisenberg Manoeuvre: by trying to observe the behaviour of my code, you change the behaviour of my code.
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Economists call this a "network effect": the more people there are on Twitter, the more reason there is to be on Twitter and the harder it is to leave. But technologists have another name for this: "lock in." The more you pour into Twitter, the more it costs you to leave. Economists have a name for that cost: the "switching cost."
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- Jan 2021
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www.washingtonpost.com www.washingtonpost.com
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In light of that reality, the Times should consider junking its language about journalists being “perceived as biased.” Thanks to the work of unprincipled folks in American politics, the mere expression of an opinion, or an emotion, is now viewed as evidence of ingrained bias. Opinion and bias are not the same.
Bericht über die Kündigung Lauren Wolfes durch die New York Times. Ist doppelt interessant—wegen der Problematik des Social Media Engagements von Journalistinnen und wegen der Aussagen zum Meinungsklima in den USA.
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- Nov 2020
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news.ycombinator.com news.ycombinator.com
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I've spent the last 3.5 years building a platform for "information applications". The key observation which prompted this was that hierarchical file systems didn't work well for organising information within an organisation.However, hierarchy itself is still incredibly valuable. People think in terms of hierarchies - it's just that they think in terms of multiple hierarchies and an item will almost always belong in more than one place in those hierarchies.If you allow users to describe items in the way which makes sense to them, and then search and browse by any of the terms they've used, then you've eliminated almost all the frustrations of a file system. In my experience of working with people building complex information applications, you need: * deep hierarchy for classifying things * shallow hierarchy for noting relationships (eg "parent company") * multi-values for every single field * controlled values (in our case by linking to other items wherever possible) Unfortunately, none of this stuff is done well by existing database systems. Which was annoying, because I had to write an object store.
Impressed by this comment. It foreshadows what Roam would become:
- People think in terms of items belonging to multiple hierarchies
- If you allow users to describe items in a way that makes sense to them and allow them to search and browse by any of the terms they've used, you've solved many of the problems of existing file systems
What you need to build a complex information system is:
- Deep hierarchies for classifying things (overlapping hierarchies should be possible)
- Shallow hierarchies for noting relationships (Roam does this with a flat structure)
- Multi-values for every single field
- Controlled values (e.g. linking to other items when possible)
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www.nytimes.com www.nytimes.com
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The creation of these types of fake images only became possible in recent years thanks to a new type of artificial intelligence called a generative adversarial network. In essence, you feed a computer program a bunch of photos of real people. It studies them and tries to come up with its own photos of people, while another part of the system tries to detect which of those photos are fake.
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- Oct 2020
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Maia, H. P., Ferreira, S. C., & Martins, M. L. (2020). Adaptive network approach for emergence of societal bubbles. ArXiv:2010.08635 [Nlin, Physics:Physics]. http://arxiv.org/abs/2010.08635
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www.scientificamerican.com www.scientificamerican.com
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Centola, D. (n.d.). Why Social Media Makes Us More Polarized and How to Fix It. Scientific American. Retrieved October 25, 2020, from https://www.scientificamerican.com/article/why-social-media-makes-us-more-polarized-and-how-to-fix-it/
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covid-19.iza.org covid-19.iza.org
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COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved October 10, 2020, from https://covid-19.iza.org/publications/dp13574/
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journals.sagepub.com journals.sagepub.com
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I first briefly lay out alternative media theory as it existed prior to the dominance of Facebook, Google, and Twitter.
I've been thinking about it for a while but even if all social sites were interoperable, I suspect that a small handful of 2 or 3 would have the largest market share. This is as the result of some of the network theory and research found in Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life by Alberto-Llaszlo Barabasi
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blog.joinmastodon.org blog.joinmastodon.org
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So that’s already a huge advantage over other platforms due the basic design. And in my opinion it’s got advantages over the other extreme, too, a pure peer-to-peer design, where everyone would have to fend for themselves, without the pooled resources.
Definitely something the IndieWeb may have to solve for.
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Mastodon deliberately does not support arbitrary search. If someone wants their message to be discovered, they can use a hashtag, which can be browsed. What does arbitrary search accomplish? People and brands search for their own name to self-insert into conversations they were not invited to. What you can do, however, is search messages you posted, received or favourited. That way you can find that one message on the tip of your tongue.
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appliednetsci.springeropen.com appliednetsci.springeropen.com
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First, I will focus in these larger groups because reviews that transcend the boundary between the social and natural sciences are rare, but I believe them to be valuable. One such review is Borgatti et al. (2009), which compares the network science of natural and social sciences arriving at a similar conclusion to the one I arrived.
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For instance, in the study of mobile phone networks, the frequency and length of interactions has often been used as measures of link weight (Onnela et al. 2007), (Hidalgo and Rodriguez-Sickert 1008), (Miritello et al. 2011).
And they probably shouldn't because typically different levels of people are making these decisions. Studio brass and producers typically have more to say about the lead roles and don't care as much about the smaller ones which are overseen by casting directors or sometimes the producers. The only person who has oversight of all of them is the director, and even then they may quit caring at some point.
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heterogeneous networks have been found to be effective promoters of the evolution of cooperation, since there are advantages to being a cooperator when you are a hub, and hubs tend to stabilize networks in equilibriums where levels of cooperation are high (Ohtsuki et al. 2006), (Pacheco et al. 2006), (Lieberman et al. 2005), (Santos and Pacheco 2005).
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www.twitterandteargas.org www.twitterandteargas.org
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His weak-tie networks had been politically activated
This makes me wonder if she's cited Mark Granovetter or any of similar sociologists yet?
Apparently she did in footnote 32 in chapter 1. Ha!
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Only a segment of the population needs to be connected digitally to affect the entire environment. In Egypt in 2011, only 25 percent of the population of the country was on-line, with a smaller portion of those on Facebook, but these people still managed to change the wholesale public discussion, including conversa-tions among people who had never been on the site.
There's some definite connection to this to network theory of those like Stuart Kaufmann. You don't need every node to be directly connected to create a robust network, particularly when there are other layers--here interpersonal connections, cellular, etc.
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scripting.com scripting.com
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Anyone who's dealt with networks knows that the network knows more than the individual."
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hapgood.us hapgood.us
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A stunning thing that we forget, but the link here is not part of the author’s intent, but of the reader’s analysis. The majority of links in the memex are made by readers, not writers. On the world wide web of course, only an author gets to determine links.
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www.eugenewei.com www.eugenewei.com
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After all, the best messaging app in most countries or continents is the one most other people are already using there.
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www.economist.com www.economist.com
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“INFORMATION RULES”—published in 1999 but still one of the best books on digital economics—Carl Shapiro and Hal Varian, two economists, popularised the term “network effects”,
I want to get a copy of this book.
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Ghavasieh, A., Nicolini, C., & De Domenico, M. (2020). Statistical physics of complex information dynamics. ArXiv:2010.04014 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2010.04014
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Houghton, J. P. (2020). Interdependent Diffusion: The social contagion of interacting beliefs. ArXiv:2010.02188 [Physics]. http://arxiv.org/abs/2010.02188
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link.aps.org link.aps.org
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Gaisbauer, F., Olbrich, E., & Banisch, S. (2020). Dynamics of opinion expression. Physical Review E, 102(4), 042303. https://doi.org/10.1103/PhysRevE.102.042303
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link.aps.org link.aps.org
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Burda, Z., Kotwica, M., & Malarz, K. (2020). Ageing of complex networks. Physical Review E, 102(4), 042302. https://doi.org/10.1103/PhysRevE.102.042302
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arxiv.org arxiv.org
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Merlino, L. P., Pin, P., & Tabasso, N. (2020). Debunking Rumors in Networks. ArXiv:2010.01018 [Physics]. http://arxiv.org/abs/2010.01018
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www.deutschlandfunk.de www.deutschlandfunk.de
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Ausführliche Sendung über Desinformationstechniken vor allem im Umkreis der Trump-Kampagne, viele Hinweise auf weitere Ressourcen
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- Sep 2020
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iriss.stanford.edu iriss.stanford.edu
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2020 Conference on Computational Sociology | IRiSS. (n.d.). Retrieved 30 September 2020, from https://iriss.stanford.edu/css/conferences/2020-conference-computational-sociology
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Chen, Q., & Porter, M. A. (2020). Epidemic Thresholds of Infectious Diseases on Tie-Decay Networks. ArXiv:2009.12932 [Physics]. http://arxiv.org/abs/2009.12932
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Humphries, R., Mulchrone, K., Tratalos, J., More, S., & Hövel, P. (2020). A Systematic Framework of Modelling Epidemics on Temporal Networks. ArXiv:2009.11965 [Nlin, Physics:Physics]. http://arxiv.org/abs/2009.11965
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twitter.com twitter.com
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Pausal Živference on Twitter. (n.d.). Twitter. Retrieved September 26, 2020, from https://twitter.com/PausalZ/status/1309208611265093632
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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.
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Kojaku, S., Livan, G., & Masuda, N. (2020). Detecting citation cartels in journal networks. ArXiv:2009.09097 [Physics]. http://arxiv.org/abs/2009.09097
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Peel, L., & Schaub, M. T. (2020). Detectability of hierarchical communities in networks. ArXiv:2009.07525 [Physics, Stat]. http://arxiv.org/abs/2009.07525
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Ehlert, A., Kindschi, M., Algesheimer, R., & Rauhut, H. (2020). Human social preferences cluster and spread in the field. Proceedings of the National Academy of Sciences, 117(37), 22787–22792. https://doi.org/10.1073/pnas.2000824117
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en.wikipedia.org en.wikipedia.org
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Network nodes are the points of connection of the transmission medium to transmitters and receivers of the electrical
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psyarxiv.com psyarxiv.com
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Lee, Hyeon-seung, Derek Dean, Tatiana Baxter, Taylor Griffith, and Sohee Park. ‘Deterioration of Mental Health despite Successful Control of the COVID-19 Pandemic in South Korea’. Preprint. PsyArXiv, 30 August 2020. https://doi.org/10.31234/osf.io/s7qj8.
Tags
- South Korea
- demographic
- COVID-19
- public health
- social factors
- depression
- physical health
- lang:en
- social distancing
- loneliness
- is:preprint
- psychological outcome
- anxiety
- crisis
- general population
- females
- psychosis-risk
- behavioural science
- stress
- social network
- nationwide lockdown
- mental health
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www.springer.com www.springer.com
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Kiss, I. Z., Miller, J., & Simon, P. L. (2017). Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer International Publishing. https://doi.org/10.1007/978-3-319-50806-1
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arxiv.org arxiv.org
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Miller, J. C., & TIng, T. (2019). EoN (Epidemics on Networks): A fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks. Journal of Open Source Software, 4(44), 1731. https://doi.org/10.21105/joss.01731
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www.scientificamerican.com www.scientificamerican.com
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Stix, G. (n.d.). Zoom Psychiatrists Prep for COVID-19’s Endless Ride. Scientific American. Retrieved June 9, 2020, from https://www.scientificamerican.com/article/zoom-psychiatrists-prep-for-covid-19s-endless-ride1/
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news.northeastern.edu news.northeastern.edu
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How network science models can predict the next stages of the COVID-19 pandemic. (n.d.). Retrieved June 10, 2020, from https://news.northeastern.edu/2020/05/14/how-network-science-models-can-predict-the-next-stages-of-the-covid-19-pandemic/
Tags
- network
- COVID-19
- interview
- loosening restrictions
- modeling
- school
- prediction
- reopening
- lang:en
- university
- epidemiology
- is:news
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www.pmo.gov.sg www.pmo.gov.sg
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katherine_chen. (2020, June 17). PMO | National Broadcast by PM Lee Hsien Loong on 7 June 2020 [Text]. Prime Minister’s Office Singapore; katherine_chen. http://www.pmo.gov.sg/Newsroom/National-Broadcast-PM-Lee-Hsien-Loong-COVID-19
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r/BehSciAsk—Integrating Behavioural Science into Epidimiology. (n.d.). Reddit. Retrieved June 27, 2020, from https://www.reddit.com/r/BehSciAsk/comments/hg501h/integrating_behavioural_science_into_epidimiology/
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www.visualcapitalist.com www.visualcapitalist.com
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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/
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kwokchain.com kwokchain.com
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By bringing both designers and non-designers alike into Figma, they create a cross-side network effect. In a direct network effect, a homogenous group gets more value from a product as more of them join. In contrast, a cross-side network effect involves two (or more) distinct groups that grow in size and value as the other group does, too. Figma’s cross-side network effect between designers and non-designers is one of the primary and under-appreciated sources of their compounding success over the last few years. As more designers use Figma, they pull in the non-designers they work with. Similarly, as these non-designers use Figma, they encourage the other designers they work with to use Figma. It’s a virtuous circle and a powerful compounding loop.
By bringing non-designers into the design process, Figma created cross-side network effects for itself.
Where typically the designers would get their designer peers to use the tools they're excited about, now non-designers would experience the value and recommend Figma to designers and non-designers alike.
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arxiv.org arxiv.org
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Carmona, H. A., de Noronha, A. W. T., Moreira, A. A., Araujo, N. A. M., & Andrade Jr, J. S. (2020). Cracking urban mobility. ArXiv:2008.13644 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2008.13644
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- Aug 2020
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www.pnas.org www.pnas.org
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Thurner, S., Klimek, P., & Hanel, R. (2020). A network-based explanation of why most COVID-19 infection curves are linear. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2010398117
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link.aps.org link.aps.org
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Perez, I. A., Di Muro, M. A., La Rocca, C. E., & Braunstein, L. A. (2020). Disease spreading with social distancing: A prevention strategy in disordered multiplex networks. Physical Review E, 102(2), 022310. https://doi.org/10.1103/PhysRevE.102.022310
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Moya, C., Cruz y Celis Peniche, P. D., Kline, M. A., & Smaldino, P. (2020). Dynamics of Behavior Change in the COVID World [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/kxajh
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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
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en.wikipedia.org en.wikipedia.org
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MQTT
Message Queuing Telemetry Transport MQTT protocol -> publish-subscribe network protocol that transports messages between devices, usually through TCP/IP
Protocol defines 2 network entities:
- message broker -> receives messages from clients and then sends them to any clients subscribed to topic.
- a number of clients ->
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psyarxiv.com psyarxiv.com
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Papini, S., Dainer-Best, J., Rubin, M., Zaizar, E. D., Telch, M. J., & Smits, J. A. J. (2020). Psychological Networks can Identify Potential Pathways to Specific Intervention Targets for Anxiety in Response to Coronavirus Disease 2019 (COVID-19) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/4zr25
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Tags
- is:preprint
- COVID-19
- Bayesian
- prediction
- contact tracing
- lang:en
- app
- containment
- digital solution
- likelihood
- limitation
- network model
Annotators
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arxiv.org arxiv.org
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en.wikipedia.org en.wikipedia.org
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Since onion services can receive incoming connections even if they are behind a router doing network address translation (NAT), TorChat does not need any port forwarding to work.
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Gou, W., Huang, S., Chen, J., Li, X., & Chen, Q. (2020). Structural and Dynamic of Global Population Migration Network. ArXiv:2006.02208 [Physics]. http://arxiv.org/abs/2006.02208
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Velásquez-Rojas, F., da Silva, P. C. V., Connaughton, C., Moreno, Y., Rodrigues, F. A., & Vazquez, F. (2020). Disease and information spreading at different speeds in multiplex networks. ArXiv:2006.01965 [Physics]. http://arxiv.org/abs/2006.01965
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Mariani, M. S., & Lü, L. (2020). Network-based ranking in social systems: Three challenges. Journal of Physics: Complexity, 1(1), 011001. https://doi.org/10.1088/2632-072X/ab8a61
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Eroglu, D. (2020). Revealing Dynamics, Communities, and Criticality from Data. Physical Review X, 10(2). https://doi.org/10.1103/PhysRevX.10.021047
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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
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arxiv.org arxiv.org
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Blanchard, M. A., & Heeren, A. (2020). Why We Should Move from Reductionism and Embrace a Network Approach to Parental Burnout? [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/y34cq
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arxiv.org arxiv.org
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Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
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psyarxiv.com psyarxiv.com
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Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c
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Porter, E. & Wood. T.J. (2020 May 14). Why is Facebook so afraid of checking facts? Wired. https://www.wired.com/story/why-is-facebook-so-afraid-of-checking-facts/
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Katz, D. M., Coupette, C., Beckedorf, J., & Hartung, D. (2020). Complex Societies and the Growth of the Law. ArXiv:2005.07646 [Physics]. http://arxiv.org/abs/2005.07646
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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
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psyarxiv.com psyarxiv.com
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Fränken, J.-P., & Pilditch, T. (2020). Cascades across networks are sufficient for the formation of echo chambers: An agent-based model. https://doi.org/10.31234/osf.io/8rgkc
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Vaidyanathan, G. (2020). People power: How India is attempting to slow the coronavirus. Nature, 580(7804), 442–442. https://doi.org/10.1038/d41586-020-01058-5
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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
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Batty, M. (2020). The Coronavirus crisis: What will the post-pandemic city look like?: Environment and Planning B: Urban Analytics and City Science, 47(4), https://doi.org/10.1177/2399808320926912
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Ball, P. (2020). Anti-vaccine movement could undermine efforts to end coronavirus pandemic, researchers warn. Nature, 581(7808), 251–251. https://doi.org/10.1038/d41586-020-01423-4
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Pichler, Anton, Marco Pangallo, R. Maria del Rio-Chanona, François Lafond, and J. Doyne Farmer. “Production Networks and Epidemic Spreading: How to Restart the UK Economy?” ArXiv:2005.10585 [Physics, q-Fin], May 21, 2020. http://arxiv.org/abs/2005.10585.
Tags
- consumption
- social distincing
- COVID-19
- transmission rate
- production
- GDP
- United Kingdom
- lang:en
- input-output constraints
- reopening industry
- inventory dynamics
- economics
- industry
- unemployment
- economic growth
- epidemic spreading
- demand
- work from home
- production network
- supply
- epidemiology
- is:article
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Johnson, N.F., Velásquez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature (2020). https://doi.org/10.1038/s41586-020-2281-1
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Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaid, M., & Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences, 201917687. https://doi.org/10.1073/pnas.1917687117
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Masuda, N., & Holme, P. (2020). Small inter-event times govern epidemic spreading on networks. Physical Review Research, 2(2), 023163. https://doi.org/10.1103/PhysRevResearch.2.023163
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arxiv.org arxiv.org
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Tuninetti, M., Aleta, A., Paolotti, D., Moreno, Y., & Starnini, M. (2020). Prediction of scientific collaborations through multiplex interaction networks. ArXiv:2005.04432 [Physics]. http://arxiv.org/abs/2005.04432
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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/
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Vasques Filho, D., & O’Neale, D. R. J. (2020). Transitivity and degree assortativity explained: The bipartite structure of social networks. Physical Review E, 101(5), 052305. https://doi.org/10.1103/PhysRevE.101.052305
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Riolo, M. A., & Newman, M. E. J. (2020). Consistency of community structure in complex networks. Physical Review E, 101(5), 052306. https://doi.org/10.1103/PhysRevE.101.052306
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Hope, T., Borchardt, J., Portenoy, J., Vasan, K., & West, J. (2020, May 6). Exploring the COVID-19 network of scientific research with SciSight. Medium. https://medium.com/ai2-blog/exploring-the-covid-19-network-of-scientific-research-with-scisight-f75373320a8c
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nesslabs.com nesslabs.com
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In Roam, each note becomes a node in your mind map, and there’s no central concept you have to start with. The map is fluid, with no hierarchy, no stacking, no linearity. It’s a giant knowledge web representing your notes and the connections between them.
Just like the brain works.
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www.nature.com www.nature.com
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Vespignani, A., Tian, H., Dye, C. et al. Modelling COVID-19. Nat Rev Phys (2020). https://doi.org/10.1038/s42254-020-0178-4
Tags
- emergency
- quarentine
- COVID-19
- public health
- prediction
- isolation
- transmission dynamics
- antibody testing
- infection
- open data
- lang:en
- policy
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- superspreading
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- modeling
- mathematics
- contact tracing
- intervention
- computational modeling
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- epidemiology
- pharmaceutical
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arxiv.org arxiv.org
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Pastor-Satorras, R., & Castellano, C. (2020). The localization of non-backtracking centrality in networks and its physical consequences. ArXiv:2005.03913 [Physics]. http://arxiv.org/abs/2005.03913
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arxiv.org arxiv.org
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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
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www.nature.com www.nature.com
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Li, A., Zhou, L., Su, Q., Cornelius, S. P., Liu, Y.-Y., Wang, L., & Levin, S. A. (2020). Evolution of cooperation on temporal networks. Nature Communications, 11(1), 1–9. https://doi.org/10.1038/s41467-020-16088-w
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