| 摘 要: 社交网络对于个人及社会的重要性日益凸显。随着社交网络数据规模的不断扩大，如何清晰美观地展现 社交网络关系结构成为信息可视化领域研究的一大难点。针对此研究难点，本文应用网络理论和实验领域的专家之间 的合作关系数据集，通过度中心性、介数中心性指标发现数据中的关键节点，改进差分进化算法的变异、交叉和选择过 程，提出了基于差分进化的社交网络可视化布局算法，有效减少初始位置对可视化结果的影响，并且最终呈现的可视化 结果可以清楚美观地展现社交网络结构。 |
| 关键词: 社交网络；可视化；差分进化；关键节点 |
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中图分类号: TP391.9
文献标识码: A
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| Study on Visualization of Social Network Based on Differential Evolution |
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BI Luqi,YANG Lianhe
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( School of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China)
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| Abstract: Social networks have become increasingly prominent for both individuals and the society.As social network data continues to grow in size, how to clearly and attractively display the social network relationship structure has become a major difficulty in the field of information visualization. In view of the difficulty of this research,this paper applies the cooperation relationship data between experts in network theory and experimentation to find key nodes in the data through degree-centrality and betweenness-centrality indicators to improve the variation,crossover and selection of differential evolution algorithms.Therefore,a social network visual layout algorithm based on differential evolution is proposed,which effectively reduces the impact of the initial position on the visualization results.The visual results presented finally can clearly and beautifully reflect the social network structure. |
| Keywords: social network;visualization;differential evolution;key nodes |
