Archive for the ‘Networks in the News’ Category

How algorithmic popularity bias hinders or promotes quality


By Giovanni Luca Ciampaglia, Azadeh Nematzadeh, Filippo Menczer & Alessandro Flammini Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the ...

Complexity Explorables by Dirk Brockmann


I herd you!” enables you to explore how different network structures impact the spread of a disease in a population. Consequently, you can understand a phenomenon called “herd immunity”, defined that “a disease can be eradicated even if not the entire population is immunized.” The webpage is simple, yet very informative. If you’re interested, there are many other examples and models. Check them out at!

Multidimensional Understanding of Tie Strength


An article “The weakness of tie strength” in the current issue of Social Networks unpacked three elements related to the strength of ties: capacity, frequency, and redundancy. The case with an email network shows that the three elements are not highly correlated and are likely to reflect different dimensions of ties. This multidimensional view may explain some unexpected empirical findings. For example, Garg and Telang (forthcoming in Management Science) found that strong ties in online social networks play a significant role in job search and weak ties are ineffective. Weak ties may generate some job information, but only strong ties lead to actions such as referrals.

Using networks to analyze soccer


Engineering professor Luís Amaral has investigated complex social and structural networks in areas ranging from healthcare and biology to gender discrimination and gun violence. His diverse research interests and innate curiosity eventually led him to study soccer — his favorite sport.

Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies


A recent research from West Virginia University shows how to detect opioid addicted people from large structured heterogeneous networks using transductive learning. Check out the paper:

How network theory predicts the value of Bitcoin


A recent research by Spencer Wheatley at ETH Zurich in Switzerland and a few colleagues shows that the key measure of value for cryptocurrencies is the network of people who use them. What’s more, they say, once Bitcoin is valued in this way it becomes possible to see when it is overvalued and perhaps even to spot the telltale signs that a market crash is imminent. Read the complete article here: And the article published by MIT Technology Review here:

Educational Game Website for Network Concepts


“It’s Nicky Case!” is a cool website where you can play different kinds of games related to network concepts such as complex contagion and small-world networks. Moreover, there are other games for game theory and theory of voting. Check out the website (!

Computational Social Science ≠ Computer Science + Social Data


Hanna Wallach published a thought piece of what computational social science is, especially from her computer science point of view. Given computational social science in mind, She made points of differences between computer science and social science in terms of goals, models, data, and challenges:  Goals: Prediction vs. explanation — “[C]omputer scientists may be interested in finding the needle in the haystack—such as […] the right Web page to display from a search—but social scientists are more commonly interested in characterizing the haystack.” Models: “Models for prediction are often intended to replace human interpretation or reasoning, whereas models for explanation are intended to inform or guide human reasoning.” Data: “Computer scientists usually work with large-scale, digitized datasets, often collected and made available for no particular purpose other than “machine learning research.” In contrast, social scientists often use data collected or curated in order to answer specific questions.” Challenges: Datasets consisting of social phenomena raised ethical concerns regarding privacy, fairness, and accountability — “they may be new to most computer scientists, but they are not new to social scientists.”   She concludes her article saying that “we need to work with social scientists in order to understand the ethical implications and consequences of our modeling decisions.” The article is available here.    

Cooperation, clustering, and assortative mixing in dynamic networks


A recent study by David Melamed and his colleagues examined whether the emergent structures that promote cooperation are driven by reputation or can emerge purely via dynamics. To answer the research question, they recruited 1,979 Amazon Mechanical Turkers and asked them to play an iterated prisoner’s dilemma game. Further, these participants were randomly assigned one of 16 experimental conditions. Results of the experiments show that dynamic networks yield high rates of cooperation even without reputational knowledge. Additionally, the study found that the targeted choice condition in static networks yields cooperation rates as high as those in dynamic networks. The original article is available here.

Students’ social interactions and daily routines to make predictions about freshman retention


Sudha Ram’s Smart Campus research tracks students’ social interactions and daily routines via their CatCard usage — and leverages that information to make predictions about freshman retention. The goal of Ram’s Smart Campus research is to help educational institutions repurpose the data already being captured from student ID cards to identify those most at risk for not returning after their first year of college. Ram found that social integration and routine were stronger predictors than end-of-term grades, which is one of the more traditionally used predictors of freshman retention in higher education. Read the article here.