基于改进标签传播算法的舆情社交网络社区发现OA北大核心
Community discovery of public opinion social network based on improved label propagation algorithm
通过改进的标签传播算法研究了舆情社交网络中的社交主题发现.针对传统算法容易陷入局部最优的问题,依据节点间相似度选择标签传播时的邻居节点;针对传统算法标签更新时的随机性问题,通过结合舆论动力学模型HK的观点交互过程,依据节点影响力的大小更新标签.实验结果表明,该方法在最好情况下(k=0.9)相较于原算法,在稳定性和模块度指标两方面分别提高了 31％和78％,并且优于其他几种改进算法.由此可见,该算法相较于原算法及其他改进算法在舆情社交网络的主题社区发现中表现更好.
This paper studied the discovery of social topics in social networks using an improved label propagation algorithm.To address the problem of traditional algorithms easily falling into local optima,it selected neighbor nodes during label propa-gation based on the similarity between nodes.To solve the randomness issue in label updates of traditional algorithms,it used the node influence to update labels by incorporating the opinion interaction process from the HK opinion dynamics model.The experimental results show that the proposed method,in the best case(k=0.9),improves stability by 31％and modularity by 78％compared to the original algorithm and outperforms several other improved algorithms.It demonstrates that the proposed algorithm performs better in discovering topic communities in social opinion networks compared to the original algorithm and other improved algorithms.
钱晓东;王卓
兰州交通大学经济与管理学院,兰州 730070兰州交通大学电子与信息工程学院,兰州 730070
计算机与自动化
标签传播算法舆情社交网络HK模型主题社区发现
label propagation algorithmpublic opinion social networkHK modeltopic community discovery
《计算机应用研究》 2025 (001)
48-55 / 8
甘肃省自然科学基金资助项目(23JRRA898)
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