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Multilayer networks
- Mikko Kivelä, A. Arenas, M. Barthelemy, J. Gleeson, Y. Moreno, M. Porter
- Computer ScienceJ. Complex Networks
- 27 September 2013
This chapter shows how interconnected multilayer topology describes such networks more accurately than edge coloring does and introduces the tensor formalism used to construct them.
Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
- M. Karsai, Mikko Kivelä, J. Saramäki
- BiologyPhysical review. E, Statistical, nonlinear, and…
- 10 June 2010
The time evolution of information propagation is followed through communication networks by using empirical data on contact sequences and the susceptible-infected model and introducing null models where event sequences are appropriately shuffled to distinguish between the contributions of different impeding effects.
Characterizing the Community Structure of Complex Networks
- Andrea Lancichinetti, Mikko Kivelä, J. Saramäki, S. Fortunato
- Computer SciencePLoS ONE
- 24 May 2010
A systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks finds that the mesoscopic organization of networks of the same category is remarkably similar.
Sequential algorithm for fast clique percolation.
- J. Kumpula, Mikko Kivelä, K. Kaski, J. Saramäki
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 10 May 2008
A sequential clique percolation algorithm (SCP) is presented to do fast community detection in weighted and unweighted networks, for cliques of a chosen size and can simultaneously produce a dendrogram representation of hierarchical community structure.
Generalizations of the clustering coefficient to weighted complex networks.
- J. Saramäki, Mikko Kivelä, J. Onnela, K. Kaski, J. Kertész
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 30 August 2006
A comparative study of the several suggestions of the clustering coefficient, which is one of the central characteristics in the complex network theory, is presented.
A comparative study of social network models: Network evolution models and nodal attribute models
- R. Toivonen, L. Kovanen, Mikko Kivelä, J. Onnela, J. Saramäki, K. Kaski
- Computer ScienceSoc. Networks
- 5 May 2008
EDENetworks: A user‐friendly software to build and analyse networks in biogeography, ecology and population genetics
- Mikko Kivelä, S. Arnaud-Haond, J. Saramäki
- Computer ScienceMolecular Ecology Resources
- 1 January 2015
EDENetworks aims to fill this void by providing an easy‐to‐use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically.
Multiscale analysis of spreading in a large communication network
- Mikko Kivelä, R. K. Pan, K. Kaski, J. Kertész, J. Saramäki, M. Karsai
- Computer ScienceArXiv
- 19 December 2011
A multiscale analysis shows that for the spreading velocity, time-domain inhomogeneities are as important as the network topology, which indicates the need to take time- domain information into account when studying spreading dynamics.
Cumulative effects of triadic closure and homophily in social networks
- A. Asikainen, G. Iñiguez, Javier Ureña-Carrion, K. Kaski, Mikko Kivelä
- PsychologyScience Advances
- 17 September 2018
The model shows that even small individual bias may prompt network-level changes such as segregation or core group dominance, and highlights that individual-level mechanisms should not be analyzed separately without considering the dynamics of society as a whole.
Estimating inter-event time distributions from finite observation periods in communication networks
- Mikko Kivelä, M. Porter
- BusinessPhysical review. E, Statistical, nonlinear, and…
- 29 December 2014
This paper models event sequences using stationary renewal processes, and formulates simple heuristics for determining the severity of the bias, and finds that correcting for the bias can lead to qualitatively different results for the tails of the IET distributions.
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