动态网络链接预测方法的研究
发布时间：2019-03-14 20:53
【摘要】：随着互联网的普及,网络数据呈现爆炸式增长,如何从中找出有用的信息成为研究者们关注的焦点。作为链接挖掘的一个重要分支,链接预测通过对已知网络进行建模分析,挖掘节点与链接形成的关系,从而为人们提供有价值的信息。现实网络具有动态性和稀疏性的特点。一方面,网络是动态发展的,随着时间的推移,网络中节点和链接的数量都在不断更新。节点之间形成链接的时间信息通常可以反应出节点之间的潜在关系。采用链接形成的时间属性,对预测将来新链接的形成有着重要的意义。另一方面,大规模网络中节点的数量很大,但节点间形成链接的数目却相对较少,导致网络异常稀疏。如果能从中选取一些具有代表性且对分类器有较大改进的节点对样本,则可以缓解训练压力且保持较好的预测效果。针对以上问题,本课题主要在以下三个方面展开研究:1、对现有的链接预测方法进行综述。总结了近年来一些著名的链接预测方法,提出了目前链接预测任务存在的瓶颈和挑战,重点介绍动态网络链接预测方法的研究现状。2、针对网络动态性的特点,提出了一种基于集成学习的动态链接预测方法,称为Dynamic。传统的链接预测方法大多针对网络的静态结构预测隐含的链接,而忽视了网络在动态演变过程中的潜在信息。本课题提出的方法使用机器学习技术对网络结构特征的变化进行训练,学习每种结构特征的变化并得到一个分类器,最终采用集成学习方法将每个分类器加权得到预测结果。3、针对网络稀疏性的特点,本课题在网络进化及链接预测过程中引入主动学习范式,提出一种新的基于主动学习的动态链接预测方法,称为DynActive。该方法为网络中每个结构特征的变化序列都生成一个分类器,再利用这些分类器对每个未连接的节点对进行评分把预测结果差异较大的节点对样本交由用户判别,一旦获取真实的标记,系统采用更新的训练集重新训练各分类器并整合到最终的模型。在三个合著者网络数据集的实验结果证明,在动态网络链接预测方法中引入集成学习和主动学习,AUC指标均得到了显著提高。
[Abstract]:With the popularity of the Internet, the network data shows explosive growth, how to find out useful information from it has become the focus of researchers' attention. As an important branch of link mining, link prediction provides valuable information for people by modeling and analyzing known networks and mining the relationship between nodes and links. The real network is dynamic and sparse. On the one hand, the network is dynamic, with the passage of time, the number of nodes and links in the network is constantly updated. The time information of the link between nodes can usually reflect the potential relationship between nodes. It is of great significance to predict the formation of new links in the future by using the time attribute of link formation. On the other hand, the number of nodes in large-scale networks is very large, but the number of links between nodes is relatively small, resulting in the network is extremely sparse. If we can select some representative node pairs which can improve the classifier greatly, the training pressure can be alleviated and the prediction effect can be maintained. In view of the above problems, this paper mainly focuses on the following three aspects: 1, the existing link prediction methods are summarized. This paper summarizes some famous link prediction methods in recent years, puts forward the bottleneck and challenge existing in the link prediction task at present, and emphatically introduces the research status of dynamic network link prediction methods. 2, aiming at the characteristics of network dynamics, This paper presents a dynamic link prediction method based on integrated learning, which is called Dynamic.. Most of the traditional link prediction methods focus on the hidden links in the static structure prediction of the network, but ignore the potential information of the network in the process of dynamic evolution. The method proposed in this paper uses machine learning technology to train the changes of network structure features, to learn the changes of each structure feature and to obtain a classifier. Finally, the ensemble learning method is used to weight each classifier to get the prediction result. 3, according to the characteristics of network sparsity, this paper introduces the active learning paradigm in the process of network evolution and link prediction. A new dynamic link prediction method based on active learning is proposed, which is called DynActive.. The proposed method generates a classifier for each sequence of structural features in the network, and then uses these classifiers to grade each unconnected pair of nodes to give the samples of node pairs whose prediction results are different from each other to be judged by the user. Once the real markers are obtained, the system retrains the classifiers with the updated training set and integrates them into the final model. The experimental results of three co-authors' network data sets show that the AUC index is significantly improved by the introduction of integrated learning and active learning into the dynamic network link prediction method.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP181
本文编号：2440362
[Abstract]:With the popularity of the Internet, the network data shows explosive growth, how to find out useful information from it has become the focus of researchers' attention. As an important branch of link mining, link prediction provides valuable information for people by modeling and analyzing known networks and mining the relationship between nodes and links. The real network is dynamic and sparse. On the one hand, the network is dynamic, with the passage of time, the number of nodes and links in the network is constantly updated. The time information of the link between nodes can usually reflect the potential relationship between nodes. It is of great significance to predict the formation of new links in the future by using the time attribute of link formation. On the other hand, the number of nodes in large-scale networks is very large, but the number of links between nodes is relatively small, resulting in the network is extremely sparse. If we can select some representative node pairs which can improve the classifier greatly, the training pressure can be alleviated and the prediction effect can be maintained. In view of the above problems, this paper mainly focuses on the following three aspects: 1, the existing link prediction methods are summarized. This paper summarizes some famous link prediction methods in recent years, puts forward the bottleneck and challenge existing in the link prediction task at present, and emphatically introduces the research status of dynamic network link prediction methods. 2, aiming at the characteristics of network dynamics, This paper presents a dynamic link prediction method based on integrated learning, which is called Dynamic.. Most of the traditional link prediction methods focus on the hidden links in the static structure prediction of the network, but ignore the potential information of the network in the process of dynamic evolution. The method proposed in this paper uses machine learning technology to train the changes of network structure features, to learn the changes of each structure feature and to obtain a classifier. Finally, the ensemble learning method is used to weight each classifier to get the prediction result. 3, according to the characteristics of network sparsity, this paper introduces the active learning paradigm in the process of network evolution and link prediction. A new dynamic link prediction method based on active learning is proposed, which is called DynActive.. The proposed method generates a classifier for each sequence of structural features in the network, and then uses these classifiers to grade each unconnected pair of nodes to give the samples of node pairs whose prediction results are different from each other to be judged by the user. Once the real markers are obtained, the system retrains the classifiers with the updated training set and integrates them into the final model. The experimental results of three co-authors' network data sets show that the AUC index is significantly improved by the introduction of integrated learning and active learning into the dynamic network link prediction method.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP181
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