• Scalable temporal latent space inference for link prediction in dynamic social networks (extended abstract)

      Zhu, Linhong; Guo, Dong; Yin, Junming; Ver Steeg, Greg; Galstyan, Aram; Department of Management Information Systems, University of Arizona (IEEE, 2017-04)
      Understanding and characterizing the processes driving social interactions is one of the fundamental problems in social network research. A particular instance of this problem, known as link prediction, has recently attracted considerable attention in various research communities. Link prediction has many important commercial applications, e.g., recommending friends in an online social network such as Facebook and suggesting interesting pins in a collection sharing network such as Pinterest. This work is focused on the temporal link prediction problem: Given a sequence of graph snapshots G1, · ··, Gt from time 1 to t, how do we predict links in future time t + 1? To perform link prediction in a network, one needs to construct models for link probabilities between pairs of nodes. A temporal latent space model is proposed that is built upon latent homophily assumption and temporal smoothness assumption. First, the proposed modeling allows to naturally incorporate the well-known homophily effect (birds of a feather flock together). Namely, each dimension of the latent space characterizes an unobservable homogeneous attribute, and shared attributes tend to create a link in a network.
    • Sharing and Commenting Facilitate Political Learning on Facebook: Evidence From a Two-Wave Panel Study

      Kim, D.H.; Weeks, B.E.; Lane, D.S.; Hahn, L.B.; Kwak, N.; University of Arizona (SAGE Publications Ltd, 2021)
      Social media, as sources of political news and sites of political discussion, may be novel environments for political learning. Many early reports, however, failed to find that social media use promotes gains in political knowledge. Prior research has not yet fully explored the possibility based on the communication mediation model that exposure to political information on social media facilitates political expression, which may subsequently encourage political learning. We find support for this mediation model in the context of Facebook by analyzing a two-wave survey prior to the 2016 U.S. presidential election. In particular, sharing and commenting, not liking or opinion posting, may facilitate political knowledge gains. © The Author(s) 2021.