192,851 research outputs found
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Non-Conservative Diffusion and its Application to Social Network Analysis
The random walk is fundamental to modeling dynamic processes on networks.
Metrics based on the random walk have been used in many applications from image
processing to Web page ranking. However, how appropriate are random walks to
modeling and analyzing social networks? We argue that unlike a random walk,
which conserves the quantity diffusing on a network, many interesting social
phenomena, such as the spread of information or disease on a social network,
are fundamentally non-conservative. When an individual infects her neighbor
with a virus, the total amount of infection increases. We classify diffusion
processes as conservative and non-conservative and show how these differences
impact the choice of metrics used for network analysis, as well as our
understanding of network structure and behavior. We show that Alpha-Centrality,
which mathematically describes non-conservative diffusion, leads to new
insights into the behavior of spreading processes on networks. We give a
scalable approximate algorithm for computing the Alpha-Centrality in a massive
graph. We validate our approach on real-world online social networks of Digg.
We show that a non-conservative metric, such as Alpha-Centrality, produces
better agreement with empirical measure of influence than conservative metrics,
such as PageRank. We hope that our investigation will inspire further
exploration into the realms of conservative and non-conservative metrics in
social network analysis
Entropy Rate of Diffusion Processes on Complex Networks
The concept of entropy rate for a dynamical process on a graph is introduced.
We study diffusion processes where the node degrees are used as a local
information by the random walkers. We describe analitically and numerically how
the degree heterogeneity and correlations affect the diffusion entropy rate. In
addition, the entropy rate is used to characterize complex networks from the
real world. Our results point out how to design optimal diffusion processes
that maximize the entropy for a given network structure, providing a new
theoretical tool with applications to social, technological and communication
networks.Comment: 4 pages (APS format), 3 figures, 1 tabl
How Homophily Affects Learning and Diffusion in Networks
We examine how three different communication processes operating through social networks are affected by homophily - the tendency of individuals to associate with others similar to themselves. Homophily has no effect if messages are broadcast or sent via shortest paths; only connection density matters. In contrast, homophily substantially slows learning based on repeated averaging of neighbors' information and Markovian diffusion processes such as the Google random surfer model. Indeed, the latter processes are strongly affected by homophily but completely independent of connection density, provided this density exceeds a low threshold. We obtain these results by establishing new results on the spectra of large random graphs and relating the spectra to homophily. We conclude by checking the theoretical predictions using observed high school friendship networks from the Adolescent Health dataset.Networks, Learning, Diffusion, Homophily, Friendships, Social Networks, Random Graphs, Mixing Time, Convergence, Speed of Learning, Speed of Convergence
AN APPROACH TO APPROXIMATE DIFFUSION PROCESSES IN SOCIAL NETWORKS
Social network analysis is concerned with the analysis of influence of an individual within a social network and how the influence diffuses through the network. It has been shown useful in business analytics. In this paper, we extend a nonlinear dynamical system that accurately models virus propagation in epidemiology to model information diffusion in social networks. Our approach can numerically calculate each node’s probability to get activated given the initial active set. It provides an alternative way of estimating the number of nodes reached by the initial target set in the diffusion process. We validate our approach by comparing its predicting performance with diffusion simulations. Using the number of nodes reached in the diffusion process as an influence measure, our results show that the proposed method can provide a way of identifying nontrivial nodes as influencer
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