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

We have published two new papers in December:

1. The Node Vector Distance Problem in Complex Networks, by M. Coscia, A. Gomez-Lievano, F. Neffke, published in ACM Computing Surveys
   
   The paper develops a new measure to quantify the distances between sets of nodes, with important applications on network dynamics such as spread of diseases.
   Read Michele’s blog post about it: https://www.michelecoscia.com/?p=1898
2. Data-driven strategies for optimal bicycle network growth, by L.G. Natera Orozco, F. Battiston, G. Iniguez, M. Szell, published in Royal Society Open Science
   
   Here we investigate the network structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks.

We have published four new papers over the summer:

1. New and atypical combinations: An assessment of novelty and interdisciplinarity, by M. Fontana, M. Iori, F. Montobbio, R. Sinatra, published in Research Policy
   
   We compare different indicators of novelty and interdisciplinarity, and find that often they can’t distinguish novel and non-novel articles apart. We also find measured novelty highly overlaps measured interdisciplinarity, because the two are operationalized in similar ways.
2. Mapping socioeconomic indicators using social media advertising data, by M. Fatehkia, I. Tingzon, A. Orden, S. Sy, V. Sekara, M. Garcia-Herranz, I. Weber, published in EPJ Data Science
   
   In this paper we ask: Is it possible to estimate poverty using data from the Facebook Ad platform?
3. Knowledge diffusion in the network of international business travel, by M. Coscia, F.M.H. Neffke, R. Hausmann, published in Nature Human Behavior
   
   Read Michele’s blog post about it: http://www.michelecoscia.com/?p=1838
4. Multiplex Graph Association Rules for Link Prediction, by M. Coscia and M. Szell, accepted for publication in 15th International Conference on Web and Social Media (ICWSM) 2021
   
   Here we investigate the multiplex link prediction problem with graph association rules: Will two nodes connect, and of which type?

NERDS are back from the lockdown with two three new papers published today:

1. Distortions of political bias in crowdsourced misinformation flagging, by Michele Coscia and Luca Rossi, published in the Journal of the Royal Society Interface
   
   Luca writes more about the paper here: https://blogit.itu.dk/lucarossi/2020/06/10/reasonable-wrong-technical-solutions-to-social-problems/
   And here Michele’s take: http://www.michelecoscia.com/?p=1816

2. Extracting the multimodal fingerprint of urban transportation networks, by Luis Guillermo Natera Orozco, Federico Battiston, Gerardo Iñiguez, and Michael Szell, published in Transport Findings
   
   In this paper we analyze urban transport network layers of multiple cities and come up with a multiplex-network based method to construct a “fingerprint” of how these layers connect. This gives insights and a classification on the multimodal potential of cities (how their modes of transport are connected).

Update June 11th: Make that one more:

3. Generalized Euclidean Measure to Estimate Network Distances, by Michele Coscia, published in ICWSM-2020

Luca and I have published a paper on the ASONAM conference — happening this week. In it, we discuss the effects of performing a projection from a bipartite to a unipartite network, and then filtering the edges according to some way to find statistically significant thresholds. You can read more details on my blog: http://www.michelecoscia.com/?p=1699