Description: Personal web-site of Victor Amelkin
computer science (1326) data mining (574) algorithms (268) social networks (181) university of pennsylvania (53) network science (18) victor amelkin (1) warren center for network and data sciences (1) economic networks (1) systems and control (1)
Existing socio-psychological studies show that the process of opinion formation is inherently a network process, with user opinions in a social network being attracted to a certain average opinion. One simple and intuitive incarnation of this notion of an opinion attractor is the average π T x of user opinions x weighted by the users' eigenvector centralities π . This value is a lucrative target for control, as altering it essentially changes the mass opinion in the network. Since any potentially malicious
In this work, we assume that the adversary aims to maliciously change the network's average opinion by altering the opinions of some unknown users. We, then, state an NP-hard problem— DIVER —of disabling such network opinion control attempts via strategically altering the network's users' eigenvector centralities by recommending a limited number of links to the users. Relying on Markov chain theory, we provide perturbation analysis that shows how eigencentrality and, hence, our problem's objective change in
In this work, we introduce the Social Network Distance (SND) —a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure