Identifying Influential Nodes in Social Networks: Exploiting Self-Voting Mechanism
Panfeng Liu1, Longjie Li1,2, Yanhong Wen1
1School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Big Data
|April 21, 2023
Summary
The VoteRank* algorithm introduces self-voting and H-index for influence maximization, outperforming existing methods in network analysis. This approach better reflects real-world scenarios for identifying key network nodes.
Area of Science:
- Network Science
- Computer Science
- Data Analysis
Background:
- The influence maximization (IM) problem is crucial for applications like viral marketing and disease control.
- Existing methods like VoteRank use voting approaches but exclude self-voting, which is unrealistic in many scenarios.
Purpose of the Study:
- To address the limitations of existing influence maximization algorithms.
- To propose a novel algorithm, VoteRank*, incorporating self-voting and node diversity.
Main Methods:
- Introduced a self-voting mechanism into the voting process for influence maximization.
- Utilized the H-index to measure node voting ability and influence spread to neighbors.
Main Results:
- VoteRank* demonstrated superior performance compared to baseline methods.
- Experimental validation on 12 benchmark networks confirmed the algorithm's effectiveness.
Conclusions:
- The self-voting mechanism and H-index-based approach enhance influence maximization.
- VoteRank* offers a more realistic and effective solution for identifying influential nodes in networks.
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