144 research outputs found
A general Markov chain approach for disease and rumour spreading in complex networks
Spreading processes are ubiquitous in natural and artificial systems. They can be studied via a plethora of models, depending on the specific details of the phenomena under study. Disease contagion and rumour spreading are among the most important of these processes due to their practical relevance. However, despite the similarities between them, current models address both spreading dynamics separately. In this article, we propose a general spreading model that is based on discrete time Markov chains. The model includes all the transitions that are plausible for both a disease contagion process and rumour propagation. We show that our model not only covers the traditional spreading schemes but that it also contains some features relevant in social dynamics, such as apathy, forgetting, and lost/recovering of interest. The model is evaluated analytically to obtain the spreading thresholds and the early time dynamical behaviour for the contact and reactive processes in several scenarios. Comparison with Monte Carlo simulations shows that the Markov chain formalism is highly accurate while it excels in computational efficiency. We round off our work by showing how the proposed framework can be applied to the study of spreading processes occurring on social networks
A trust model for spreading gossip in social networks
We introduce here a multi-type bootstrap percolation model, which we call
T-Bootstrap Percolation (T-BP), and apply it to study information propagation
in social networks. In this model, a social network is represented by a graph G
whose vertices have different labels corresponding to the type of role the
person plays in the network (e.g. a student, an educator, etc.). Once an
initial set of vertices of G is randomly selected to be carrying a gossip (e.g.
to be infected), the gossip propagates to a new vertex provided it is
transmitted by a minimum threshold of vertices with different labels. By
considering random graphs, which have been shown to closely represent social
networks, we study different properties of the T-BP model through numerical
simulations, and describe its implications when applied to rumour spread, fake
news, and marketing strategies.Comment: 9 pages, 9 figure
Dynamic Resource Management in Clouds: A Probabilistic Approach
Dynamic resource management has become an active area of research in the
Cloud Computing paradigm. Cost of resources varies significantly depending on
configuration for using them. Hence efficient management of resources is of
prime interest to both Cloud Providers and Cloud Users. In this work we suggest
a probabilistic resource provisioning approach that can be exploited as the
input of a dynamic resource management scheme. Using a Video on Demand use case
to justify our claims, we propose an analytical model inspired from standard
models developed for epidemiology spreading, to represent sudden and intense
workload variations. We show that the resulting model verifies a Large
Deviation Principle that statistically characterizes extreme rare events, such
as the ones produced by "buzz/flash crowd effects" that may cause workload
overflow in the VoD context. This analysis provides valuable insight on
expectable abnormal behaviors of systems. We exploit the information obtained
using the Large Deviation Principle for the proposed Video on Demand use-case
for defining policies (Service Level Agreements). We believe these policies for
elastic resource provisioning and usage may be of some interest to all
stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note:
substantial text overlap with arXiv:1209.515
Randomised Algorithms on Networks
Networks form an indispensable part of our lives. In particular, computer networks have ranked amongst the most influential networks in recent times. In such an ever-evolving and fast growing network, the primary concern is to understand and analyse different aspects of the network behaviour, such as the quality of service and efficient information propagation. It is also desirable to predict the behaviour of a large computer network if, for example, one of the computers is infected by a virus. In all of the aforementioned cases, we need protocols that are able to make local decisions and handle the dynamic changes in the network topology. Here, randomised algorithms are preferred because many deterministic algorithms often require a central control. In this thesis, we investigate three network-based randomised algorithms, threshold load balancing with weighted tasks, the pull-Moran process and the coalescing-branching random walk. Each of these algorithms has extensive applicability within networks and computational complexity within computer science.
In this thesis we investigate threshold-based load balancing protocols. We introduce a generalisation of protocols in [2, 3] to weighted tasks.
This thesis also analyses an evolutionary-based process called the death-birth update, defined here as the Pull-Moran process. We show that a class of strong universal amplifiers does not exist for the Pull-Moran process. We show that any class of selective amplifiers in the (standard) Moran process is a class of selective suppressors under the Pull-Moran process. We then introduce a class of selective amplifiers called Punk graphs.
Finally, we improve the broadcasting time of the coalescing-branching (COBRA) walk analysed in [4], for random regular graphs. Here, we look into the COBRA approach as a randomised rumour spreading protocol
Fundamentals of spreading processes in single and multilayer complex networks
Spreading processes have been largely studied in the literature, both
analytically and by means of large-scale numerical simulations. These processes
mainly include the propagation of diseases, rumors and information on top of a
given population. In the last two decades, with the advent of modern network
science, we have witnessed significant advances in this field of research. Here
we review the main theoretical and numerical methods developed for the study of
spreading processes on complex networked systems. Specifically, we formally
define epidemic processes on single and multilayer networks and discuss in
detail the main methods used to perform numerical simulations. Throughout the
review, we classify spreading processes (disease and rumor models) into two
classes according to the nature of time: (i) continuous-time and (ii) cellular
automata approach, where the second one can be further divided into synchronous
and asynchronous updating schemes. Our revision includes the heterogeneous
mean-field, the quenched-mean field, and the pair quenched mean field
approaches, as well as their respective simulation techniques, emphasizing
similarities and differences among the different techniques. The content
presented here offers a whole suite of methods to study epidemic-like processes
in complex networks, both for researchers without previous experience in the
subject and for experts.Comment: Review article. 73 pages, including 24 figure
Interacting Spreading Processes in Multilayer Networks: A Systematic Review
© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward
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