融合多用户属性的网络知识社区核心用户识别研究:基于情感加权的LeaderRank算法

Identifying Core Users in Online Knowledge Community by Integrating Multiple User Attributes:Based on the Emotion-Weighted LeaderRank Algorithm

基于对网络知识社区用户属性的分析,本文提出一种融合多用户属性的核心用户识别方法,以提升核心用户识别的效率和效果,为提高社区运营和管理水平提供理论和方法参考。首先,基于用户的基本属性数据对用户的活跃性和专业性进行量化;其次,构建网络知识社区超网络模型,提出基于邻居好友重叠度的用户社交关系重要性算法、用户交互活动中的累计交互情感计算方法以及用户综合情感倾向性排名算法;最后,采用熵权法融合上述指标作为用户核心性得分,并通过得分排序识别核心用户。研究结果表明,相比于用户社交关系网络中的度中心性排名和用户交互关系网络中的LeaderRank排名,本文提出的融合多属性的网络知识社区核心用户识别方法具有更好的识别效果。

Based on the analysis of user attributes in network knowledge communities,a core user identification method that integrates multiple user attributes is proposed to improve the efficiency and effect of core user identification and provide theoretical and methodological reference for improving community operation and management levels.First,based on the user’s basic attribute data,the user’s activity and professionalism are quantified.Second,a hypernetwork model of the online knowledge community is constructed.An algorithm for the importance of user social relations based on the overlap of neighboring friends,a method for calculating cumulative interaction emotions in user interaction activities,and a ranking algorithm for user comprehensive emotional orientation are proposed.Finally,the entropy weight method is used to integrate the above indicators as the user’s core score,and core users are identified by sorting the scores.The results of em‐pirical research indicate that,compared with the degree centrality ranking in the user social relationship network and the LeaderRank ranking in the user interaction relationship network,the method for identifying core users in the online knowl‐edge community by integrating multiple attributes proposed in this study has better recognition effects.