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现代图书情报技术  2016, Vol. 32 Issue (3): 8-17    DOI: 10.11925/infotech.1003-3513.2016.03.02
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
语义社会网络的超网络模型构建及关键节点自动化识别方法研究*
张磊,马静(),李丹丹,沈洋
南京航空航天大学经济与管理学院 南京 210016
Hypernetwork Model for Semantic Social Network and Automatic Identification of Key Nodes
Zhang Lei,Ma Jing(),Li Dandan,Shen Yang
College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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摘要 

目的】通过对语义社会网络的建模, 讨论如何识别对舆论传播演化起核心作用的关键节点。【方法】引入超网络理论对微博语义社会网络进行理论建模, 使用情感本体以及LDA话题模型对数据实现节点量化, 提出超边排序算法对用户节点进行计算和排序从而获取关键节点。【结果】利用真实微博网络数据编程实现超网络模型的构建和量化, 通过结果分析证明本文的关键节点识别方法在实际应用场景中的有效性和准确性。【局限】关键节点识别方法的实时应用效果和对识别关键节点后如何有效引导和干预机制未能全面涉及。【结论】本文的关键节点识别方法能够挖掘出微博网络的关键节点, 为政府对网络舆情监管和引导提供一种解决方案, 减少负面内容和消极舆论对互联网健康发展的影响。

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张磊
马静
李丹丹
沈洋
关键词 超网络语义社会网络关键节点识别LDA模型情感本体    
Abstract

[Objective] This study aims to identify the key nodes of public opinion spread and evolution based on the semantic social network model. [Methods] We first built model for Weibo semantic social network with the help of hypernetwork theory, and then used emotion Ontology and LDA model to quantify nodes. Finally, we established the hyper edge sorting algorithm to identify the key nodes. [Results] The proposed model could effectively and acturately quantify those nodes from real Weibo data. [Limitations] We did not explore the results of the proposed method’s real-time performance, and new ways of leading the public opinion after identifying those key nodes. [Conclusions] This study provides a solution for the government to identify the key nodes in the social network systems, and then reduce the impacts of negative contents to the healthy development of the Internet.

Key wordsHypernetwork    Semantic social network    Key node identification    LDA model    Emotion Ontology
收稿日期: 2015-10-08     
基金资助:*本文系国家自然科学基金项目“基于演化本体的网络舆情自适应跟踪方法研究”(项目编号:71373123)、江苏高校哲学社会科学研究重点项目“基于超网络的江苏教育微博舆情多元意见演化模型及应用研究”(项目编号:2015ZDIXM007)和校基本科研业务费重大项目培育基金“基于‘模型-数据双驱动’的复杂社会网络行为大数据分析方法研究”(项目编号:P201630X)的研究成果之一
引用本文:   
张磊,马静,李丹丹,沈洋. 语义社会网络的超网络模型构建及关键节点自动化识别方法研究*[J]. 现代图书情报技术, 2016, 32(3): 8-17.
Zhang Lei,Ma Jing,Li Dandan,Shen Yang. Hypernetwork Model for Semantic Social Network and Automatic Identification of Key Nodes. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2016.03.02.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.03.02
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