Author Archive

Scale-free Networks Are Rare


Recently Aaron Clauset and his colleague share their new study: “Scale-free networks are rare”. In this study, they found scale-free network structure is not so prevalent based on their statistical analyses of almost 1000 network datasets across different domains. In particular, their results indicate only 4% of the datasets showing the strongest-possible evidence of scale-free structure and 52% demonstrating the weakest-possible evidence. Additionally, this study has invoked intense conversations over Twitter. For instance, Laszlo Barabasi retweeted Aaron Caluset’s tweet, saying “Every 5 years someone is shocked to re-discover that a pure power law does not fit many networks. True: Real networks have predictable deviations. Hence forcing a pure power law on these is like…fitting a sphere to the cow. Sooner or later the hoof will stick out.” Link to the paper: Link to Barabasi’s retweet:

A Mechanistic Model of Human Network Recall


Recently, Omodei, Brashears, and Arenas published a paper about describing a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior based on experimental data. They found that human recall is based on accurate recall of a hub of high degree actors and also uses compression heuristics (i.e., schemata simplifying the encoding and recall of social information) for both structural and affective information. The original paper is here:

The Social Bow Tie


A recent study investigated a new way to identify the strength of ties. Using two different large datasets, the researchers found that for each pair of individuals, a bow tie structure of the network itself is strongly associated with the strength of ties between them that the researchers measure in other ways. The abstract of the paper is as follows: Understanding tie strength in social networks, and the factors that influence it, have received much attention in a myriad of disciplines for decades. Several models incorporating indicators of tie strength have been proposed and used to quantify relationships in social networks, and a standard set of structural network metrics have been applied to predominantly online social media sites to predict tie strength. Here, we introduce the concept of the “social bow tie” framework, a small subgraph of the network that consists of a collection of nodes and ties that surround a tie of interest, forming a topological structure that resembles a bow tie. We also define several intuitive and interpretable metrics that quantify properties of the bow tie. We use random forests and regression models to predict categorical and continuous measures of tie strength from different properties of the bow tie, including ...

How does network structure influence the wisdom of crowds?


Researchers at Annenberg School for Communication, University of Pennsylvania recently published a paper about “Network dynamics of social influence in the wisdom of crowds” in PNAS. They conducted an online network experiment where participants were asked to estimate numeric quantity (e.g., the caloric content) and tested how the accuracy of group estimates changes in different communication networks. They found that in decentralized networks, the group estimates were improved and in centralized networks, the accuracy of group estimates was undermined. Read the full article here.

Bad bots do good: Random artificial intelligence helps people coordinate


“To figure out whether random AI can help people coordinate, Hirokazu Shirado, a sociologist and systems engineer, and Nicholas Christakis, a sociologist and physician, both at Yale University, asked volunteers to play a simple online game. Each person controlled one node among 20 in a network. The nodes were colored green, orange, or purple, and people could change their node color at any time. The goal was for no two adjacent nodes to share the same color, but players could see only their color and the colors of the nodes to which they were connected, so sometimes settling conflicts with neighbors raised unseen conflicts between those neighbors and their neighbors. If the network achieved the goal before the 5-minute time limit was up, all players in the network received extra payment. The researchers recruited 4000 players and placed them in 230 randomly generated networks. Some of the networks had 20 people controlling the nodes, but others had three of the most central or well-connected nodes already colored in such a way that they fit one of the solutions. (Each network had multiple solutions.) And some of the networks had 17 people and three bots, or simple AI programs, in charge of the nodes. In some networks, ...