“Small steps to better understanding”

November 04, 2019

Exploring Concurrency and Reachability in the Presence of High Temporal Resolution

Network properties govern the rate and extent of spreading processes on networks, from simple contagions to complex cascades. Recent advances have extended the study of spreading processes from static networks to temporal networks, where nodes and links appear and disappear. We review previous studies on the effects of temporal connectivity for understanding the spreading rate and outbreak size of model infection processes. We focus on the effects of “accessibility”, whether there is a temporally consistent path from one node to another, and “reachability”, the density of the corresponding “accessibility graph” representation of the temporal network. We study reachability in terms of the overall level of temporal concurrency between edges, quantifying the overlap of edges in time. We explore the role of temporal resolution of contacts by calculating reachability with the full temporal information as well as with a simplified interval representation approximation that demands less computation. We demonstrate the extent to which the computed reachability changes due to this simplified interval representation. 

July 22, 2019

Homophily and minority size explain perception biases in social networks

People's perceptions about the size of minority groups in social networks can
be biased, often showing systematic over- or underestimation. These social
perception biases are often attributed to biased cognitive or motivational
processes. Here we show that both over- and underestimation of the size of a
minority group can emerge solely from structural properties of social networks.
Using a generative network model, we show analytically that these biases depend
on the level of homophily and its asymmetric nature, as well as on the size of
the minority group. Our model predictions correspond well with empirical data
from a cross-cultural survey and with numerical calculations on six real-world
networks. We also show under what circumstances individuals can reduce their
biases by relying on perceptions of their neighbors. This work advances our
understanding of the impact of network structure on social perception biases
and offers a quantitative approach for addressing related issues in society.

May 10, 2019

Impact of perception models on friendship paradox and opinion formation

Topological heterogeneities of social networks have a strong impact on the individuals embedded in those networks. One of the interesting phenomena driven by such heterogeneities is the friendship paradox (FP), stating that the mean degree of one's neighbors is larger than the degree of oneself. Alternatively, one can use the median degree of neighbors as well as the fraction of neighbors having a higher degree than oneself. Each of these reflects on how people perceive their neighborhoods, i.e., their perception models, hence how they feel peer pressure. In our paper, we study the impact of perception models on the FP by comparing three versions of the perception model in networks generated with a given degree distribution and a tunable degree-degree correlation or assortativity. The increasing assortativity is expected to decrease network-level peer pressure, while we find a nontrivial behavior only for the mean-based perception model. By simulating opinion formation, in which the opinion adoption probability of an individual is given as a function of individual peer pressure, we find that it takes the longest time to reach consensus when individuals adopt the median-based perception model compared to other versions. Our findings suggest that one needs to consider the proper perception model for better modeling human behaviors and social dynamics.

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