See the published version of this preprint at Computational Social Networks.
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Research
Vesa Kuikka
Finnish Defence Research Agency, Tykkikentäntie 1, PO BOX 10, 11311 Riihimäki, Finland
Corresponding Author
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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.
Keywords
Complex networks, Community detection, Influence spreading model, Network connectivity, Probability matrix, E-mail forwarding process
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CURRENT STATUS: Published
View the published article at Computational Social Networks.
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Posted 06 Dec, 2020
Editorial decision: Major Revision
On 06 Dec, 2020
Review #2 received
Received 23 Nov, 2020
Review #1 received
Received 17 Nov, 2020
Reviewer #2 agreed
On 29 Oct, 2020
Reviewer #1 agreed
On 19 Oct, 2020
Reviewers invited
Invitations sent on 15 Oct, 2020
Editor assigned
On 11 Jul, 2020
Submission checks complete
On 10 Jul, 2020
Editor invited
On 10 Jul, 2020
This version was not preprinted
Version 1
Posted 09 Mar, 2020
No community comments so far
Editorial decision: Major Revision
On 21 Jun, 2020
Review #2 received
Received 19 Jun, 2020
Review #1 received
Received 18 May, 2020
Reviewer #2 agreed
On 27 Apr, 2020
Reviewer #1 agreed
On 31 Mar, 2020
Reviewers invited
Invitations sent on 27 Mar, 2020
Editor assigned
On 03 Mar, 2020
First submitted
On 02 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
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Subject Areas
Theoretical Computer Science
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See the published version of this preprint at Computational Social Networks.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Vesa Kuikka
Finnish Defence Research Agency, Tykkikentäntie 1, PO BOX 10, 11311 Riihimäki, Finland
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Computational Social Networks.
Version (no preprint)
Posted 06 Dec, 2020
Editorial decision: Major Revision
On 06 Dec, 2020
Review #2 received
Received 23 Nov, 2020
Review #1 received
Received 17 Nov, 2020
Reviewer #2 agreed
On 29 Oct, 2020
Reviewer #1 agreed
On 19 Oct, 2020
Reviewers invited
Invitations sent on 15 Oct, 2020
Editor assigned
On 11 Jul, 2020
Submission checks complete
On 10 Jul, 2020
Editor invited
On 10 Jul, 2020
This version was not preprinted
Version 1
Posted 09 Mar, 2020
No community comments so far
Editorial decision: Major Revision
On 21 Jun, 2020
Review #2 received
Received 19 Jun, 2020
Review #1 received
Received 18 May, 2020
Reviewer #2 agreed
On 27 Apr, 2020
Reviewer #1 agreed
On 31 Mar, 2020
Reviewers invited
Invitations sent on 27 Mar, 2020
Editor assigned
On 03 Mar, 2020
First submitted
On 02 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Theoretical Computer Science
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Computational Social 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.
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
This preprint is available for download as a PDF.
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