Temporal evolution of tagging subnetwork features and motif under different activity levels - take the QA community Zhihu as an example

Xin Feng; Liangxuan Li; Jiapei LiMeiru CuiLiming SunYe Wu

摘要

Purpose - This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu. Design/methodology/approach - A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif. Findings - This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak. Research limitations/implications - It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks. Practical implications - This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. Social implications - Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control. Originality/value - In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.

机译：目的——本文旨在研究特征和演化规则的标记知识网络与不同的用户在问答活动水平(问答)社区由乎。设计/方法/方法——随机样本知乎问题标签由主题生成的数据网络环境被选中。质量和选择前20%和20%用户的关注,即高级用户和机器人用户,作者应用时间切片类型的数据构建标签知识网络,用qq图和ARIMA模型分析网络指标和介绍网络主题的理论和方法。这项研究表明,当权力指数度分布是小于或等于3.1,标签的ARIMA模型等级指数网络有一个更高的拟合程度。的社区,标签之间的相关性标签知识网络是非常弱的。研究局限性/意义——它不是全面、充分的对用户进行分类只根据他们的活动水平。传统的统计分析适用于大型数据集。工作,作者将进一步探索在大规模网络的特征和更长的时间尺度和考虑添加更多节点功能,包括一些边缘的特性。然后,用户是统计分类根据节点和边的属性构建复杂网络和算法等机器学习和深度学习使用计算深入研究大规模数据集知识网络的进化。影响,本文使用了真实的数据乎社区划分用户根据用户活动和理论相结合统计测试的方法,时间序列网络主题进行时间序列进化的知识网络问答社区。其他网络问题和一些新的想法。研究发现,有一个用户活动某些对标签的演变的影响网络。活动水平高的往往是稳定的,标签网络用户提供低紧随其后活动水平逐渐波动。——研究发现,用户的影响活动的发展有一定的影响标签网络。与高活动水平往往是由用户稳定,标签网络用户紧随其后较低的活动水平逐渐波动。社区,了解形成机制的网络结构和关键节点在网络有利于改善知识体系的内容,找到用户用户行为偏好和改善体验。从大量确定爆发点的话题,局部趋势和预测进行舆论引导和及时控制。选择,用户定义的质量;时间标签分为两类切片;主题进行比较和分析。统计测试,时间序列分析网络形态理论用于分析标签。

Temporal evolution of tagging subnetwork features and motif under different activity levels - take the QA community Zhihu as an example

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