Modelling community structure and temporal spreading on complex networks
We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. We illustrate the community detection methods with two small network topologies. In case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Posted 06 Dec, 2020
On 06 Dec, 2020
Received 23 Nov, 2020
Received 17 Nov, 2020
On 29 Oct, 2020
On 19 Oct, 2020
Invitations sent on 15 Oct, 2020
On 11 Jul, 2020
On 10 Jul, 2020
On 10 Jul, 2020
Posted 09 Mar, 2020
On 21 Jun, 2020
Received 19 Jun, 2020
Received 18 May, 2020
On 27 Apr, 2020
On 31 Mar, 2020
Invitations sent on 27 Mar, 2020
On 03 Mar, 2020
On 02 Mar, 2020
On 02 Mar, 2020
On 02 Mar, 2020
Modelling community structure and temporal spreading on complex networks
Posted 06 Dec, 2020
On 06 Dec, 2020
Received 23 Nov, 2020
Received 17 Nov, 2020
On 29 Oct, 2020
On 19 Oct, 2020
Invitations sent on 15 Oct, 2020
On 11 Jul, 2020
On 10 Jul, 2020
On 10 Jul, 2020
Posted 09 Mar, 2020
On 21 Jun, 2020
Received 19 Jun, 2020
Received 18 May, 2020
On 27 Apr, 2020
On 31 Mar, 2020
Invitations sent on 27 Mar, 2020
On 03 Mar, 2020
On 02 Mar, 2020
On 02 Mar, 2020
On 02 Mar, 2020
We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. We illustrate the community detection methods with two small network topologies. In case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.