1,217 research outputs found
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
Applications of Temporal Graph Metrics to Real-World Networks
Real world networks exhibit rich temporal information: friends are added and
removed over time in online social networks; the seasons dictate the
predator-prey relationship in food webs; and the propagation of a virus depends
on the network of human contacts throughout the day. Recent studies have
demonstrated that static network analysis is perhaps unsuitable in the study of
real world network since static paths ignore time order, which, in turn,
results in static shortest paths overestimating available links and
underestimating their true corresponding lengths. Temporal extensions to
centrality and efficiency metrics based on temporal shortest paths have also
been proposed. Firstly, we analyse the roles of key individuals of a corporate
network ranked according to temporal centrality within the context of a
bankruptcy scandal; secondly, we present how such temporal metrics can be used
to study the robustness of temporal networks in presence of random errors and
intelligent attacks; thirdly, we study containment schemes for mobile phone
malware which can spread via short range radio, similar to biological viruses;
finally, we study how the temporal network structure of human interactions can
be exploited to effectively immunise human populations. Through these
applications we demonstrate that temporal metrics provide a more accurate and
effective analysis of real-world networks compared to their static
counterparts.Comment: 25 page
Dynamic communicability predicts infectiousness
Using real, time-dependent social interaction data, we look at correlations between some recently proposed dynamic centrality measures and summaries from large-scale epidemic simulations. The evolving network arises from email exchanges. The centrality measures, which are relatively inexpensive to compute, assign rankings to individual nodes based on their ability to broadcast information over the dynamic topology. We compare these with node rankings based on infectiousness that arise when a full stochastic SI simulation is performed over the dynamic network. More precisely, we look at the proportion of the network that a node is able to infect over a fixed time period, and the length of time that it takes for a node to infect half the network.We find that the dynamic centrality measures are an excellent, and inexpensive, proxy for the full simulation-based measures
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 â âdynamic communicatorsâ 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
Graph Metrics for Temporal Networks
Temporal networks, i.e., networks in which the interactions among a set of
elementary units change over time, can be modelled in terms of time-varying
graphs, which are time-ordered sequences of graphs over a set of nodes. In such
graphs, the concepts of node adjacency and reachability crucially depend on the
exact temporal ordering of the links. Consequently, all the concepts and
metrics proposed and used for the characterisation of static complex networks
have to be redefined or appropriately extended to time-varying graphs, in order
to take into account the effects of time ordering on causality. In this chapter
we discuss how to represent temporal networks and we review the definitions of
walks, paths, connectedness and connected components valid for graphs in which
the links fluctuate over time. We then focus on temporal node-node distance,
and we discuss how to characterise link persistence and the temporal
small-world behaviour in this class of networks. Finally, we discuss the
extension of classic centrality measures, including closeness, betweenness and
spectral centrality, to the case of time-varying graphs, and we review the work
on temporal motifs analysis and the definition of modularity for temporal
graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and
Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201
Communicability across evolving networks
Many natural and technological applications generate time ordered sequences of networks, deďŹned over a ďŹxed set of nodes; for example time-stamped information about âwho phoned whoâ or âwho came into contact with whoâ arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural deďŹnition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by timeâs arrow is captured naturally through the non-mutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction
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
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