Influence of Personal Preferences on Link Dynamics in Social Networks

We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.Comment: 12 page

Analysis of Link Formation, Persistence and Dissolution in NetSense Data

We study a unique behavioral network data set (based on periodic surveys and on electronic logs of dyadic contact via smartphones) collected at the University of Notre Dame.The participants are a sample of members of the entering class of freshmen in the fall of 2011 whose opinions on a wide variety of political and social issues and activities on campus were regularly recorded - at the beginning and end of each semester - for the first three years of their residence on campus. We create a communication activity network implied by call and text data, and a friendship network based on surveys. Both networks are limited to students participating in the NetSense surveys. We aim at finding student traits and activities on which agreements correlate well with formation and persistence of links while disagreements are highly correlated with non-existence or dissolution of links in the two social networks that we created. Using statistical analysis and machine learning, we observe several traits and activities displaying such correlations, thus being of potential use to predict social network evolution

Creation, evolution, and dissolution of social groups

Understanding why people join, stay, or leave social groups is a central question in the social sciences, including computational social systems, while modeling these processes is a challenge in complex networks. Yet, the current empirical studies rarely focus on group dynamics for lack of data relating opinions to group membership. In the NetSense data, we find hundreds of face-to-face groups whose members make thousands of changes of memberships and opinions. We also observe two trends: opinion homogeneity grows over time, and individuals holding unpopular opinions frequently change groups. These observations and data provide us with the basis on which we model the underlying dynamics of human behavior. We formally define the utility that members gain from ingroup interactions as a function of the levels of homophily of opinions of group members with opinions of a given individual in this group. We demonstrate that so-defined utility applied to our empirical data increases after each observed change. We then introduce an analytical model and show that it accurately recreates the trends observed in the NetSense data

Network analysis to support public health: evolution of collaboration among leishmaniasis researchers

Submitted by Fabricia Pimenta ([email protected]) on 2017-06-28T17:06:13Z No. of bitstreams: 1 Sampaio_etal_2017.pdf: 1083702 bytes, checksum: bb5f706c28a4acc16df6b61d7a7b2698 (MD5)Approved for entry into archive by Fabricia Pimenta ([email protected]) on 2019-01-11T22:24:25Z (GMT) No. of bitstreams: 1 Sampaio_etal_2017.pdf: 1083702 bytes, checksum: bb5f706c28a4acc16df6b61d7a7b2698 (MD5)Made available in DSpace on 2019-01-11T22:24:26Z (GMT). No. of bitstreams: 1 Sampaio_etal_2017.pdf: 1083702 bytes, checksum: bb5f706c28a4acc16df6b61d7a7b2698 (MD5) Previous issue date: 2017-03-15Fundação Oswaldo Cruz. Centro de Desenvolvimento Tecnológico em Saúde. Rio de Janeiro, RJ, Brasil / National Institute for Science and Technology on Innovation on Neglected Diseases. Rio de Janeiro, RJ, Brazil / Fundação Oswaldo Cruz. Diretoria Regional de Brasilia. Brasilia, DF, Brasil.Fundação Oswaldo Cruz. Centro de Desenvolvimento Tecnológico em Saúde. Rio de Janeiro, RJ, Brasil / National Institute for Science and Technology on Innovation on Neglected Diseases. Rio de Janeiro, RJ, BrazilRensselaer Polytechnic Institute. Computer Science.Troy, NY, USA.Rensselaer Polytechnic Institute. Computer Science.Troy, NY, USA / Spoleczna Akademia Nauk. Lodz, Poland.Databases on scientific publications are a well-known source for complex network analysis. The present work focuses on tracking evolution of collaboration amongst researchers on leishmaniasis, a neglected disease associated with poverty and very common in Brazil, India and many other countries in Latin America, Asia and Africa. Using SCOPUS and PubMed databases we have identified clusters of publications resulting from research areas and collaboration between countries. Based on the collaboration patterns, areas of research and their evolution over the past 35 years, we combined different methods in order to understand evolution in science. The methods took into consideration descriptive network analysis combined with lexical analysis of publications, and the collaboration patterns represented by links in network structure. The methods used country of the authors’ publications, MeSH terms, and the collaboration patterns in seven five-year period collaboration network and publication networks snapshots as attributes. The results show that network analysis metrics can bring evidences of evolution of ollaboration between different research groups within a specific research area and that those areas have subnetworks that influence collaboration structures and focus