198 research outputs found
Social structure of Facebook networks
We study the social structure of Facebook âfriendshipâ networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributesâgender, class year, major, high school, and residenceâat these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further
What makes people bond?: A study on social interactions and common life points on Facebook
In this paper we aim at understanding if and how, by analysing people's
profile and historical data (such as data available on Facebook profiles and
interactions, or collected explicitly) we can motivate two persons to interact
and eventually create long-term bonds. We do this by exploring the relationship
between connectedness, social interactions and common life points on Facebook.
The results are of particular importance for the development of technology that
aims at reducing social isolation for people with less chances to interact,
such as older adults
Efficient inference of overlapping communities in complex networks
We discuss two views on extending existing methods for complex network
modeling which we dub the communities first and the networks first view,
respectively. Inspired by the networks first view that we attribute to White,
Boorman, and Breiger (1976)[1], we formulate the multiple-networks stochastic
blockmodel (MNSBM), which seeks to separate the observed network into
subnetworks of different types and where the problem of inferring structure in
each subnetwork becomes easier. We show how this model is specified in a
generative Bayesian framework where parameters can be inferred efficiently
using Gibbs sampling. The result is an effective multiple-membership model
without the drawbacks of introducing complex definitions of "groups" and how
they interact. We demonstrate results on the recovery of planted structure in
synthetic networks and show very encouraging results on link prediction
performances using multiple-networks models on a number of real-world network
data sets
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