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Community-based k-shell decomposition for identifying influential spreaders

Community-based k-shell decomposition for identifying influential spreaders
Community-based k-shell decomposition for identifying influential spreaders
How to identify the most influential nodes in a network for the maximization of influence spread is a great challenge. Known methods like -shell decomposition determine core nodes who individually might be the most influential spreaders for the spreading originating in a single origin. However, these techniques are not suitable for determining multiple origins that together lead to the most effective spreading. The reason is that core nodes are often found to be located closely to each other, which results in large overlapping regions rather than spreading far across the network. In this paper, we propose a new algorithm, called community-based -shell decomposition, by which a network can be viewed as multiple hierarchically ordered structures each branching off from the innermost shell to the periphery shell. To alleviate the overlap problem, our algorithm pursues a greedy strategy that preferably selects core nodes from different communities in the network, thus maximizing the joint influence of multiple origins. We systematically evaluate our algorithm against competing algorithms on multiple networks with varying network characteristics, and find that our algorithm outperforms other algorithms on networks that exhibit community structures, and the stronger communities, the better performance.
Community-based k-shell decomposition, Influential spreader, Linear threshold model
0031-3203
Sun, Peng Gang
10ea918a-a4fa-4142-a2a4-c3335001a34b
Miao, Qiguang
ff471a2d-f377-4e92-a50c-6f361ad3a392
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Sun, Peng Gang
10ea918a-a4fa-4142-a2a4-c3335001a34b
Miao, Qiguang
ff471a2d-f377-4e92-a50c-6f361ad3a392
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49

Sun, Peng Gang, Miao, Qiguang and Staab, Steffen (2021) Community-based k-shell decomposition for identifying influential spreaders. Pattern Recognition, 120, [108130]. (doi:10.1016/j.patcog.2021.108130).

Record type: Article

Abstract

How to identify the most influential nodes in a network for the maximization of influence spread is a great challenge. Known methods like -shell decomposition determine core nodes who individually might be the most influential spreaders for the spreading originating in a single origin. However, these techniques are not suitable for determining multiple origins that together lead to the most effective spreading. The reason is that core nodes are often found to be located closely to each other, which results in large overlapping regions rather than spreading far across the network. In this paper, we propose a new algorithm, called community-based -shell decomposition, by which a network can be viewed as multiple hierarchically ordered structures each branching off from the innermost shell to the periphery shell. To alleviate the overlap problem, our algorithm pursues a greedy strategy that preferably selects core nodes from different communities in the network, thus maximizing the joint influence of multiple origins. We systematically evaluate our algorithm against competing algorithms on multiple networks with varying network characteristics, and find that our algorithm outperforms other algorithms on networks that exhibit community structures, and the stronger communities, the better performance.

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Accepted/In Press date: 23 June 2021
e-pub ahead of print date: 25 June 2021
Keywords: Community-based k-shell decomposition, Influential spreader, Linear threshold model

Identifiers

Local EPrints ID: 450143
URI: http://eprints.soton.ac.uk/id/eprint/450143
ISSN: 0031-3203
PURE UUID: b82a693c-7f7f-4439-bc76-72da87d15029
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

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Date deposited: 13 Jul 2021 16:31
Last modified: 16 Jul 2021 01:48

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Contributors

Author: Peng Gang Sun
Author: Qiguang Miao
Author: Steffen Staab ORCID iD

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