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A Novel Top-k Strategy for Influence Maximization in Complex Networks with Community Structure
Jia-Lin He1,2, Yan Fu1,2, Duan-Bing Chen1,2
1Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
Plos One
|December 20, 2015
Summary
Identifying critical spreaders in complex networks is crucial for controlling processes. This study introduces a novel, balanced method for selecting spreaders in community structures, outperforming classic approaches.
Area of Science:
- Network Science
- Computational Social Science
- Information Dissemination
Background:
- Identifying critical spreaders is vital for controlling spread in complex networks.
- Existing methods struggle with community structures, often selecting clustered spreaders.
- This limitation hinders effective control strategies in real-world networks.
Purpose of the Study:
- To propose a novel method for identifying multiple critical spreaders in complex networks with community structures.
- To ensure a balanced selection of spreaders across different communities.
- To improve the effectiveness of spread control by addressing limitations of classic methods.
Main Methods:
- The proposed method divides the network into numerous super nodes.
- It then selects a specified number (k) of spreaders from these super nodes.
- This approach facilitates a balanced distribution of spreaders across communities.
Main Results:
- Experimental results demonstrate the effectiveness of the novel method.
- The method outperforms classic approaches like degree centrality, k-core, and ClusterRank.
- Superior performance is observed in networks exhibiting community structure.
Conclusions:
- The novel method offers a balanced and effective way to identify critical spreaders in networks with community structure.
- It overcomes the limitations of traditional methods by avoiding spreader clustering.
- This approach enhances the potential for controlling spreading processes in complex systems.
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