We are on a streak and just published two more papers!

1. 1. From Reddit to Wall Street: the role of committed minorities in financial collective action, by L. Lucchini, L.M. Aiello, L. Alessandretti, G. De Francisci Morales, M. Starnini and A. Baronchelli, published in Royal Society Open Science
      
      We analyzed the coordinated activity on Reddit that led to target short-selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. We found that a small fraction of individuals can trigger large behavioural cascades, and we show the role of commitment, network centrality, and social identity in such coordination process. Our findings shed light on financial collective action, which several observers anticipate will grow in importance.
   2. Generalized Euclidean Measure to Estimate Distances on Multilayer Networks, by M. Coscia, published in ACM Transactions on Knowledge Discovery from Data
      
      In this paper we propose an algorithm solving node vector distance for multilayer networks, which is a problem that arises for all kinds of network spreading processes like epidemics, economic growth, or human behavior. We do so by adapting the Mahalanobis distance, incorporating the graph’s topology via the pseudoinverse of its Laplacian. Since this is a proper generalization of the Euclidean distance in a complex space defined by the topology of the graph, and that it works on multilayer networks, we call our measure the Multi Layer Generalized Euclidean (MLGE).

We start the spring with two new papers:

1. 1. Measuring Violence: A Computational Analysis of Violence and Propagation of Image Tweets From Political Protest, by L. Rossi, C. Neumayer, J. Henrichsen, and L.K. Beck, published in Social Science Computer Review
      
      We investigated the impact of violence on the propagation of images in social media in the context of political protest. Using a computational approach, we measure the relative violence of a large set of images shared on Twitter during the protests against the G20 summit in Frankfurt am Main in 2017. This allows us to investigate if more violent content is shared more times and faster than less violent content on Twitter, and if different online communities can be characterized by the level of violence of the visual content they share. The level of violence in an image tweet does not correlate with the number of retweets and mentions it receives that the time to retweet is marginally lower for image tweets containing a high level of violence and that the level of violence in image tweets differs between communities.
   2. Automated Detection of Missing Links in Bicycle Networks, by A. Vybornova, T. Cunha, A. Gühnemann, and M. Szell, published in Geographical Analysis
      
      Here, we develop the IPDC procedure (Identify, Prioritize, Decluster, Classify) for finding the most important missing links in urban bicycle networks, using data from OpenStreetMap. In this procedure we first identify all possible gaps following a multiplex network approach, prioritize them according to a flow-based metric, decluster emerging gap clusters, and manually classify the types of gaps. We apply the IPDC procedure to Copenhagen and report the 105 top priority gaps. For evaluation, we compare these gaps with the city’s most recent Cycle Path Prioritization Plan and find considerable overlaps. Our results show how network analysis with minimal data requirements can serve as a cost-efficient support tool for bicycle network planning.
      We also developed an interactive visualization of our results at: fixbike.net

We start 2022 with two new papers!

1. 1. Epidemic dreams: dreaming about health during the COVID-19 pandemic, by S. Šćepanović, L.M. Aiello, D. Barrett and D. Quercia, published in Royal Society Open Science
      
      Luca and collaborators ask: Why were our dreams during the pandemic weird? Their computer analysis unearthed buried psychological reactions to the COVID-19 pandemic: expressions in waking life reflected a linear and logical thought process and, as such, described realistic symptoms or related disorders (e.g. nasal pain, SARS, H1N1); those in dreaming life reflected a thought process closer to the visual and emotional spheres and, as such, described either conditions unrelated to the virus (e.g. maggots, deformities, snake bites), or conditions of surreal nature (e.g. teeth falling out, body crumbling into sand).
   2. How minimizing conflicts could lead to polarization on social media: An agent-based model investigation, by M. Coscia and L. Rossi, published in PLOS ONE
       
      
       The paper explores an agent based model on how policing content and backlash on social media (i.e. conflict) can lead to an increase in polarization for both users and news sources. We find that the tendency of users and sources to avoid policing, backlash and conflict in general can increase polarization online. Specifically polarization comes from the ease of sharing political posts, intolerance for opposing points of view causing backlash and policing, and volatility in changing one’s opinion when faced with new information. On the other hand, it seems that the integrity of a news source in trying to resist the backlash and policing has little effect.
      Learn more on Michele’s blogpost.

Two new papers from the NERDS crew!

1. 1. Mapping the NFT revolution: market trends, trade networks, and visual features, by M. Nadini, L. Alessandretti, F. Di Giacinto, M. Martino, L.M. Aiello, A. Baronchelli, published in Scientific Reports
      
      Luca and collaborators performed the first large-scale analysis of the market of Non Fungible Tokens (NFTs) since its birth. The looked at 6.1 million trades of 4.7 million NFTs to learn about market, traders, visual features and price prediction. The dataset they collected is available. Learn more on this blogpost from the Alan Turing Institute.
      Also, watch the accompanying video: https://www.youtube.com/watch?v=KyIITtPKJbY
      
   2. Pearson correlations on complex networks, by M. Coscia, published in Journal of Complex Networks
       
      
       
      Estimating the correlation between two processes happening on the same network is therefore an important problem with a number of applications. However, at present there is no way to do so: current methods to estimate the correlation between two processes happening on the same network either correlate a network with itself, a single process with the network structure, or calculate a network distance between two processes. To fill this gap, Michele created a new method to extend the Pearson correlation coefficient to work on complex networks, and showed its usefulness in tasks related to social network analysis and economics. Learn more on this blogpost.

Prolific NERDS researcher Luca Maria Aiello published 2 more papers over the summer. They already received wide media coverage:

1. Streetonomics: Quantifying culture using street names, by M. Bancilhon, M. Constantinides , E.P. Bogucka, L.M. Aiello, D. Quercia, published in PLOS ONE
   

   This paper studies the names of 4,932 honorific streets in the cities of Paris, Vienna, London and New York, finding that street names greatly reflect the extent to which a society is gender biased, which professions are considered elite ones, and the extent to which a city is influenced by the rest of the world, quantifying a society’s value system.

   The paper was covered in media here:
   https://www.bbc.com/future/article/20210712-streetonomics-what-our-addresses-say-about-us
   https://www.fastcompany.com/90652762/how-streets-in-new-york-london-paris-and-vienna-got-their-names-according-to-streetonomics
   https://www.thetimes.co.uk/article/street-names-show-why-great-cities-are-worlds-apart-x06lbdwgj
   https://elpais.com/ciencia/2021-06-30/el-machismo-esta-en-las-calles.html
   https://www.lefigaro.fr/sciences/l-ame-d-une-ville-peut-elle-se-lire-dans-les-noms-de-ses-rues-20210701

2. How epidemic psychology works on Twitter: evolution of responses to the COVID-19 pandemic in the U.S., by L.M. Aiello, D. Quercia, K. Zhou, M. Constantinides, S. Šćepanović, S. Joglekar, published in Humanities and Social Sciences Communications
   
   The paper studies the use of language of 122M tweets related to the COVID-19 pandemic posted in the U.S. during the whole year of 2020. On Twitter, we identified three distinct phases. Each of them is characterized by different regimes of the three psycho-social epidemics.
   See also: https://www.fastcompany.com/90659372/pandemic-emotions-research-twitter

This month we have published four new papers, in Nature, Sustainability, IEEE Computer Graphics and Applications, and ACM Computing Surveys:

1. The universal visitation law of human mobility, by M. Schläpfer, L. Dong, K. O’Keeffe, P. Santi, M. Szell, H. Salat, S. Anklesaria, M. Vazifeh, C. Ratti, G.B. West, published in Nature
   More info at the accompanying interactive website: https://senseable.mit.edu/wanderlust/
   

   This paper reveals a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe.

2. Implementing Gehl’s Theory to Study Urban Space. The Case of Monotowns, by D. Cerrone, J. López Baeza, P. Lehtovuori, D. Quercia, R. Schifanella, and L.M. Aiello, published in Sustainability
   
   The paper presents a method to operationalize Jan Gehl’s questions for public space into metrics to map Russian monotowns’ urban life in 2017. With the use of social media data, it becomes possible to scale Gehl’s approach from the survey of small urban areas to the analysis of entire cities while maintaining the human scale’s resolution.
3. The Dreamcatcher: Interactive Storytelling of Dreams, by E.P. Bogucka, B.A. Aseniero, L.M. Aiello, D. Quercia, published in IEEE Computer Graphics and Applications
   
   Here we designed “The Dreamcatcher,” an interactive visual tool that explores the link between dreams and waking life through a collection of dream reports. We conducted a user study with 154 participants and found a 25% increase in the number of people believing that dream analysis can improve our daily lives after interacting with our tool. The visualization informed people about the potential of the continuity hypothesis to a surprising extent, to the point that it increased their concerns about sharing their own dream reports, thus opening new questions on how to design privacy-aware tools for dream collection.
4. Community Detection in Multiplex Networks, by M. Magnani, O. Hanteer, R. Interdonato, L. Rossi, A. Tagarelli, published in ACM Computing Surveys
   
   Here we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. Besides offering a much needed overview of the methods and assumptions of CD in multiplex networks the paper attempts to provide few guiding principles for the choice of a community detection approach to multiplex data. 

We have a new exciting paper out: Temporal and cultural limits of privacy in smartphone app usage by Vedran Sekara et al. published in Nature Scientific Reports, asking:

The paper looks into which apps people use and creates app-fingerprints for 3.5 million individuals. Similar to forensic science where you need 12 points to distinguish between fingerprints we ask how many apps do we need to distinguish between two users? We find people’s smartphone app behavior is very unique and 3 apps are enough to identify more than 90% of all individuals. But app-fingerprints change over time and are different between countries. We find that people have more unique app-fingerprints during summer because we use more unique apps, and Americans have the most unique fingerprints (need the fewest apps to identify them) while Finns are the least unique (need more apps to identify their fingerprint). Why is this important? Because the work highlights problems with current policies intended to protect user privacy and emphasizes that policies cannot directly be ported between countries.

In the past two years,  NERDS member Michele Coscia has been working on a textbook for the Network Analysis class he teaches at ITU. This “Atlas for the Aspiring Network Scientist”, has now reached version 1.0, and 760 pages, and is available for anyone to read for free: https://arxiv.org/abs/2101.00863

Website: https://www.networkatlas.eu/

The Atlas aims at being broad, not deep, to be a pointer to the things you need to know about Network Science rather than a deep explanation of those things.

Consider this a v1.0 of a continuous effort. There are many things to improve: language, concepts, references, figures. Please contact Michele with any comments.

Michele also plans to have interactive figures on the website in the future. Version 1.0 was all financed using his research money and time. For the future, Michele will need some support to do this in his free time. If you feel like encouraging this effort, you can consider becoming a member on Patreon.

Find a more detailed explanation of The Atlas for the Aspiring Network Scientist on Michele’s page: https://www.michelecoscia.com/?p=1913