NS Seminar: Paper Presentation

Date and Location

Oct 24, 2016 - 2:00pm to 3:00pm
Network Science Lab, Bldg 434, Room 122

Speaker

Devin Cornell, Sociology Trainee
Furkan Kocayusufoglu, CS Trainee

Abstract

Belief Network Analysis: A Relational Approach to Understanding the Structure of Attitudes

Andrei Boutyline et al.

Theories of the structure of political belief systems typically conceive of them as networks of interrelated opinions, in which some beliefs are central and others are derived from these more fundamental positions. In this paper, we formally show how such structural features can be used to construct measures of belief centrality that are based on direct comparisons of relative positions of beliefs in a network of correlations. To demonstrate the usefulness of this method and contrast it with existing techniques, we examine belief networks we construct from the 2000 American National Election Study. While regression analyses of these data have been used to argue that political beliefs are organized around cultural schemas of parenting, our structural approach contradicts this interpretation. Instead, our results are broadly consistent with the conception of political identity as a heuristic device for acquiring attitudes. To search for possible heterogeneity, we then separately examine belief networks belonging to 44 different demographic subpopulations. These analyses indicate that belief systems of different groups vary in the extent to which they are organized, but rarely vary in the logic around which they are organized. While our analyses focus on political beliefs, techniques we introduce here can be applied to many other cultural domains.

 

Predicting Positive and Negative Links in Online Social Networks

Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of  balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.