52,303 research outputs found
Quantifying and minimizing risk of conflict in social networks
Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people’s prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network’s connectivity.
Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these.
For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures.
Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk
Tuning the average path length of complex networks and its influence to the emergent dynamics of the majority-rule model
We show how appropriate rewiring with the aid of Metropolis Monte Carlo
computational experiments can be exploited to create network topologies
possessing prescribed values of the average path length (APL) while keeping the
same connectivity degree and clustering coefficient distributions. Using the
proposed rewiring rules we illustrate how the emergent dynamics of the
celebrated majority-rule model are shaped by the distinct impact of the APL
attesting the need for developing efficient algorithms for tuning such network
characteristics.Comment: 10 figure
The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure
Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
Effects of Time Horizons on Influence Maximization in the Voter Dynamics
In this paper we analyze influence maximization in the voter model with an
active strategic and a passive influencing party in non-stationary settings. We
thus explore the dependence of optimal influence allocation on the time
horizons of the strategic influencer. We find that on undirected heterogeneous
networks, for short time horizons, influence is maximized when targeting
low-degree nodes, while for long time horizons influence maximization is
achieved when controlling hub nodes. Furthermore, we show that for short and
intermediate time scales influence maximization can exploit knowledge of
(transient) opinion configurations. More in detail, we find two rules. First,
nodes with states differing from the strategic influencer's goal should be
targeted. Second, if only few nodes are initially aligned with the strategic
influencer, nodes subject to opposing influence should be avoided, but when
many nodes are aligned, an optimal influencer should shadow opposing influence.Comment: 22 page
Community structures in complex networks : detection and modeling
Complex systems are composed of a large number of interacting elements such that the system as a whole exhibits emergent properties not obvious from the properties of its individual parts. In the network approach, complex systems are represented as networks whose vertices and edges correspond to the elements and their interactions, respectively. Many networks, such as networks of protein interactions or social relationships, contain sets of densely interconnected nodes, communities, which play a concrete functional role in the original system, such as the group of proteins related to cancer metastasis. Detecting such communities in large networks has rapidly become one of the focal topics in the science of complex networks. The challenge in community detection is to define what constitutes a community in such a way that this definition not only yields meaningful communities but also allows for sufficiently fast algorithmic implementation to find them.
This thesis contributes to our understanding of community detection in complex networks in three ways. 1) The limitations of global optimization based community detection methods are analyzed. Here, the focus is on the dependence of the lower size limit of detectable communities on the tuning parameters of the methods. 2) This thesis significantly improves two community detection methods by extending their applicability domain: the Potts method is extended such that it can be applied to dense weighted networks, and a new algorithmic implementation for the k-clique percolation method is presented. The main advantage of the first method is that it allows analysis of dense weighted networks without discarding any of the link weights, whereas the advantage of the second method is its speed especially in the community analysis of weighted networks. 3) This thesis attempts to shed light on the formation of communities in networks. This is done by introducing a weighted model for social networks, whose mechanisms are based on empirical observations of social tie formation as well as observations on the topological role of tie strengths. In this model, communities emerge only if nodes sufficiently favor their strong connections in the process of establishing new ones. The model is also utilized in studies of the effects of correlations of link weights and community structure on dynamics taking place on networks. Simulations of an opinion formation model show that the dynamics is significantly slowed down due to trapping of opinions in homogenized regions corresponding to communities
Network Inference from Consensus Dynamics
We consider the problem of identifying the topology of a weighted, undirected
network from observing snapshots of multiple independent consensus
dynamics. Specifically, we observe the opinion profiles of a group of agents
for a set of independent topics and our goal is to recover the precise
relationships between the agents, as specified by the unknown network . In order to overcome the under-determinacy of the problem at hand, we
leverage concepts from spectral graph theory and convex optimization to unveil
the underlying network structure. More precisely, we formulate the network
inference problem as a convex optimization that seeks to endow the network with
certain desired properties -- such as sparsity -- while being consistent with
the spectral information extracted from the observed opinions. This is
complemented with theoretical results proving consistency as the number of
topics grows large. We further illustrate our method by numerical experiments,
which showcase the effectiveness of the technique in recovering synthetic and
real-world networks.Comment: Will be presented at the 2017 IEEE Conference on Decision and Control
(CDC
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