Networks in the News – Noshir Contractor

Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach

Koustav — Fri, 30 Nov 2018 18:42:30 +0000

A recent article “Betweenness to assess leaders in criminal networks: New evidence using the dual projection approach” by Rosanna Grassi, Francesco Calderoni, and Monica Bianchi show the performance of different betweenness centrality measures in identifying criminal leaders in a meeting participation network. Each of the measures reports different ranking of leaders and dual projection based approaches show better performance compared to traditional betweenness or flow-based measures. Read more:

How algorithmic popularity bias hinders or promotes quality

Diego — Mon, 05 Nov 2018 19:07:56 +0000

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 effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Article published at Nature Communications

https://www.nature.com/articles/s41598-018-34203-2

Complexity Explorables by Dirk Brockmann

Noshir Contractor — Mon, 15 Oct 2018 16:48:49 +0000

Complexity Explorables is a website where people easily explore some complex systems examples while playing models with fun.

For example, “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 http://www.complexity-explorables.org/explorables/!

Multidimensional Understanding of Tie Strength

Yun Huang — Fri, 06 Jul 2018 15:58:46 +0000

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

Anup Sawant — Fri, 15 Jun 2018 20:41:15 +0000

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.

https://news.northwestern.edu/stories/2018/may/using-big-data-to-analyze-soccer/

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

kre2017 — Fri, 15 Jun 2018 20:14:08 +0000

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: https://dl.acm.org/citation.cfm?id=3132857… Read the rest

How network theory predicts the value of Bitcoin

Noshir Contractor — Mon, 07 May 2018 18:58:20 +0000

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: https://arxiv.org/abs/1803.05663

And the article published by MIT Technology Review here: https://www.technologyreview.com/s/610614/how-network-theory-predicts-the-value-of-bitcoin/

Educational Game Website for Network Concepts

Noshir Contractor — Fri, 04 May 2018 15:47:17 +0000

“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 (http://ncase.me/)!… Read the rest

Computational Social Science ≠ Computer Science + Social Data

Noshir Contractor — Tue, 27 Mar 2018 15:30:57 +0000

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

Noshir Contractor — Tue, 27 Mar 2018 15:07:48 +0000

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.