Multiscale analysis of spreading in a large communication network

Kivelä, Mikko; Pan, Raj Kumar; Kaski, Kimmo; Kertész, János; Saramäki, Jari; Karsai, Márton

doi:10.1088/1742-5468/2012/03/p03005

Cited by 86 publications

(126 citation statements)

References 32 publications

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“…(21) or Eq. (27). Using these conditions, one gets A(r) = (r+−r−)(r−r0) (r++r−−2r0)r+(r++r−)r0−2r+r− .…”

mentioning

confidence: 97%

Measuring burstiness for finite event sequences

Kim

2016

Phys. Rev. E

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Characterizing inhomogeneous temporal patterns in natural and social phenomena is important to understand underlying mechanisms behind such complex systems, hence even to predict and control them. Temporal inhomogeneities in event sequences have been described in terms of bursts that are rapidly occurring events in short time periods alternating with long inactive periods. The bursts can be quantified by a simple measure, called burstiness parameter, which was introduced by Goh and Barabási [EPL 81, 48002 (2008)]. The burstiness parameter has been widely used due to its simplicity, which however turns out to be strongly affected by the finite number of events in the time series. As the finite-size effects on burstiness parameter have been largely ignored, we analytically investigate the finite-size effects of the burstiness parameter. Then we suggest an alternative definition of burstiness that is free from finite-size effects and yet simple. Using our alternative burstiness measure, one can distinguish the finite-size effects from the intrinsic bursty properties in the time series. We also demonstrate the advantages of our burstiness measure by analyzing empirical datasets.

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“…(21) or Eq. (27). Using these conditions, one gets A(r) = (r+−r−)(r−r0) (r++r−−2r0)r+(r++r−)r0−2r+r− .…”

mentioning

confidence: 97%

Measuring burstiness for finite event sequences

Kim

2016

Phys. Rev. E

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“…In epidemic spreading, it is the time it takes for a newly infected node to spread the infection further via the corresponding link. Assuming that the activations of neighbouring edges are independent [29] and that nodes become infected at uniformly random times, interactivation time distribution and waiting time distribution verify the relation [19,20] …”

Section: Diffusion and Time Ordering From Data To Modelsmentioning

confidence: 99%

Burstiness and spreading on temporal networks

2013

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We discuss how spreading processes on temporal networks are impacted by the shape of their inter-event time distributions. Through simple mathematical arguments and toy examples, we find that the key factor is the ordering in which events take place, a property that tends to be affected by the bulk of the distributions and not only by their tail, as usually considered in the literature. We show that a detailed modeling of the temporal patterns observed in complex networks can change dramatically the properties of a spreading process, such as the ergodicity of a random walk process or the persistence of an epidemic.

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“…Kempe et al [6] define a time-respecting path as a sequence of contacts with non-decreasing times. The relay time of an edge captures the time taken for a newly infected node to spread the infection further [7]. The spread of information through a network can be modeled by a cascade.…”

Section: Related Workmentioning

confidence: 99%

Temporal subgraph isomorphism

Redmond

Cunningham

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-Temporal information is increasingly available with network data sets. This information can expose underlying processes in the data via sequences of link activations. Examples range from the propagation of ideas through a scientific collaboration network, to the spread of disease via contacts between infected and susceptible individuals. We focus on the flow of funds through an online financial transaction network, in which given patterns might signify suspicious behaviour. The search for these patterns may be formulated as a temporally constrained subgraph isomorphism problem. We compare two algorithms which use temporal data at different stages during the search, and empirically demonstrate one to be significantly more efficient.

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Multiscale analysis of spreading in a large communication network

Cited by 86 publications

References 32 publications

Measuring burstiness for finite event sequences

Measuring burstiness for finite event sequences

Burstiness and spreading on temporal networks

Temporal subgraph isomorphism

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