移动通信网络中同一自然人的识别方法研究

发布时间：2018-04-17 11:55

  本文选题：社交网络 + 移动通信网络　； 参考：《中南民族大学》2015年硕士论文

【摘要】：随着移动通信技术的不断发展,越来越多的人使用手机等移动通讯工具相互联系。由于有些人从甲地到乙地工作或上学,或者为了享受移动通信运营商为新用户提供各种优惠,他们会经常更换手机号码。用户每更换一次手机号码,运营商关于该用户的各方面信息就需要重新计算和积累,不利于运营商分析业务对象特征,因此如果能将多个手机号码归属到同一自然人,则有利于运营商分析用户使用习惯,为其提供更加个性化的服务。针对移动通信网络中同一自然人的识别问题,也就是多电话号码归属同一自然人的问题,本文首先使用基于社交网络的拓扑结构相似性的方法进行了识别。基于社交网络理论,介绍了基于节点局域结构相似性、基于链接权重的节点局域相似性、融合节点和社团结构相似性、以及融合链接权重和社团结构相似性四种识别算法。在以上理论分析的基础上,基于Python语言以及NetworkX和Matplotlib算法库进行了程序编写、数据分析和算法实现。最终的实证结果表明,本文提出的基于融合链接权重和社团结构相似性的识别算法取到了很好的识别效果,具有一定的创新性。其次,本文提出了两种基于信息融合的移动通信网络中同一自然人的识别算法,一种是基于参数搜索的方式,另一种是采用支持向量机的机器学习框架。基于参数搜索方法信息融合的同一自然人识别,由于考虑了消失用户和新增用户在属性特征、网络结构和交互行为三个方面的综合相似性,采用了多参数空间搜索的方法,可降低单一特征方式的不全面性和某一维度数据缺失的局限性,具有较高的准确率。最后,本文提出了一种基于支持向量机的机器学习框架,融合了节点局域结构的相似性、共同邻居的加权相似性、社团结构相似性、节点的属性相似性以及节点之间进行信息交流的时间域相似性,取得了最佳的识别效果。本研究提出的算法较好解决了移动通信网络中同一自然人的识别问题,具有一定的商业价值。一方面本文的技术解决方案可以使移动网络运营商能更好地评估每个用户的商业价值,也可以帮助他们估计潜在用户的数量,制定吸引新用户的策略。另一方面,本文方法也可以帮助运营商利用已有的用户大数据来建立用户画像,分析用户消费习惯、潜在价值和忠诚度,通过各种营销手段来吸引或者挽留用户,以达到减小用户流失、扩大用户规模的目的。
[Abstract]:With the development of mobile communication technology, more and more people use mobile communication tools such as mobile phones to communicate with each other.Because some people work or go to school from place A to place B, or to enjoy the benefits offered by mobile operators for new users, they often change their phone numbers.Every time a user changes a mobile phone number, the operator needs to recalculate and accumulate all aspects of information about the user, which is not conducive to the operator analyzing the characteristics of the business object. Therefore, if multiple mobile phone numbers can be assigned to the same natural person,It will be helpful for operators to analyze user usage habits and provide more personalized services for them.Aiming at the problem of identifying the same natural person in the mobile communication network, that is, the multiple telephone numbers belong to the same natural person, this paper first uses the method of topology similarity based on social network to identify the same natural person.Based on the theory of social network, this paper introduces four recognition algorithms based on node local structure similarity, link weight based node local similarity, fusion node and community structure similarity, and fusion link weight and community structure similarity.Based on the above theoretical analysis, the programming, data analysis and algorithm implementation are carried out based on Python language, NetworkX and Matplotlib algorithm library.The final empirical results show that the proposed recognition algorithm based on fusion link weight and community structure similarity has good recognition effect and is innovative to some extent.Secondly, this paper proposes two recognition algorithms for the same natural person in mobile communication networks based on information fusion, one is based on parameter search, the other is a machine learning framework based on support vector machine.For the identification of the same natural person based on the information fusion of parameter search method, considering the comprehensive similarity between vanishing user and new user in attribute feature, network structure and interactive behavior, the method of multi-parameter space search is adopted.It can reduce the incomprehensiveness of a single feature and the limitation of missing data in a certain dimension, so it has a high accuracy.Finally, a machine learning framework based on support vector machine is proposed, which combines the similarity of node local structure, the weighted similarity of common neighbor, and the similarity of community structure.The attribute similarity of nodes and the time domain similarity of information exchange between nodes have obtained the best recognition effect.The algorithm proposed in this paper solves the problem of identifying the same natural person in the mobile communication network and has certain commercial value.On the one hand, the technical solution of this paper can make mobile network operators better evaluate the commercial value of each user, and also help them estimate the number of potential users and formulate strategies to attract new users.On the other hand, this method can also help operators to use existing user big data to establish user portrait, analyze user consumption habits, potential value and loyalty, through various marketing methods to attract or retain users.In order to reduce the loss of users and expand the scale of users.
【学位授予单位】：中南民族大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN929.5
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本文编号：1763505