12,555 research outputs found
Dynamic communicability and epidemic spread: a case study on an empirical dynamic contact network
We analyze a recently proposed temporal centrality measure applied to an
empirical network based on person-to-person contacts in an emergency department
of a busy urban hospital. We show that temporal centrality identifies a
distinct set of top-spreaders than centrality based on the time-aggregated
binarized contact matrix, so that taken together, the accuracy of capturing
top-spreaders improves significantly. However, with respect to predicting
epidemic outcome, the temporal measure does not necessarily outperform less
complex measures. Our results also show that other temporal markers such as
duration observed and the time of first appearance in the the network can be
used in a simple predictive model to generate predictions that capture the
trend of the observed data remarkably well.Comment: 31 pages, 15 figures, 11 tables; typos corrected; references added;
Figure 3 added; some changes to the conclusion and introductio
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
A model for dynamic communicators
We develop and test an intuitively simple dynamic network model to describe the type of time-varying connectivity structure present in many technological settings. The model assumes that nodes have an inherent hierarchy governing the emergence of new connections. This idea draws on newly established concepts in online human behaviour concerning the existence of discussion catalysts, who initiate long threads, and online leaders, who trigger feedback. We show that the model captures an important property found in e-mail and voice call data – ‘dynamic communicators’ with sufficient foresight or impact to generate effective links and having an influence that is grossly underestimated by static measures based on snaphots or aggregated data
Exploiting Temporal Complex Network Metrics in Mobile Malware Containment
Malicious mobile phone worms spread between devices via short-range Bluetooth
contacts, similar to the propagation of human and other biological viruses.
Recent work has employed models from epidemiology and complex networks to
analyse the spread of malware and the effect of patching specific nodes. These
approaches have adopted a static view of the mobile networks, i.e., by
aggregating all the edges that appear over time, which leads to an approximate
representation of the real interactions: instead, these networks are inherently
dynamic and the edge appearance and disappearance is highly influenced by the
ordering of the human contacts, something which is not captured at all by
existing complex network measures. In this paper we first study how the
blocking of malware propagation through immunisation of key nodes (even if
carefully chosen through static or temporal betweenness centrality metrics) is
ineffective: this is due to the richness of alternative paths in these
networks. Then we introduce a time-aware containment strategy that spreads a
patch message starting from nodes with high temporal closeness centrality and
show its effectiveness using three real-world datasets. Temporal closeness
allows the identification of nodes able to reach most nodes quickly: we show
that this scheme can reduce the cellular network resource consumption and
associated costs, achieving, at the same time, a complete containment of the
malware in a limited amount of time.Comment: 9 Pages, 13 Figures, In Proceedings of IEEE 12th International
Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM '11
- …