社会网络中节点角色以及群体演化研究
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摘要
二十一世纪是复杂性的世纪。作为研究复杂性科学和复杂系统的有力工具,复杂网络为研究复杂性提供了全新的视角。广泛的应用前景使得复杂网络的研究倍受国内外的密切关注,引起了不同学科的高度重视,已成为近年来的研究热点。
复杂网络是指将系统内部的各个元素抽象成为节点,元素之间的关系视为连接的边,而构成的一个具有复杂连接关系的网络。在过去的十年里,学者们对复杂网络结构特性的研究逐步深入,从最早的网络的拓扑性质研究开始,提出了节点或边的中心度量与节点间的距离度量,并揭示出现实世界中的复杂网络所具有的部分独特的统计结构特征,如：“无标度”特性、“小世界”特性以及高聚集系数等。
社会网络系指由社会个体指代节点,个体间的关联作为边形成的网络,在线社会网络系指将互联网的在线社会媒体中抽取出的实体作为节点,实体间的关联作为边形成的网络,这两类网络的研究在复杂网络研究领域获得了很多学者的关注。本论文主要对社会网络的节点影响力、节点角色,以及在线社会网络研究中话题和社会节点关联演化等问题进行了研究和探讨。分析社会网络中的节点影响力以及节点角色,进而找出社会网络中的关键节点具有很大的实用价值和现实意义。使用社会网络分析技术对在线社会媒体数据进行分析,能够发现网络用户的各种行为模式,为更多创新性的Web应用提供支持。
本论文的主要研究内容如下：
1.提出了新的影响力传播模型。论文将连续时间马尔可夫链(Continuous-Time Markov Chain, CTMC)引入经典的独立级联模型([CM),从而给出了一个改进的ICM模型,本文称之为CTMC-ICM模型。通过该模型可以得到对σ(A)的较精确的估计值,即在给定节点集合A时,能够被集合A影响到的节点数量的估计
2.基于随机游走的理论,考虑到不同节点可具有不同的传播能力,本文提出了一个新的节点影响力度量算法,称为SpreadRank度量算法。不同于过去的基于随机游走理论的中心度度量,SpreadRank将节点的传播能力引入转移矩阵函数。实验结果表明它比基于距离的中心度量方法效率更高,并且新的排序算法能够从所有节点中提取出具有影响力的节点集,通过激活这个影响力节点集合,可以将信息最大化地在网络中进行传播。
3.论文对节点的角色划分进行了分析,结合了复杂网络结构的关系属性以及节点个体属性信息,提出了一种融入结构属性的新的社会网络分析(SNA)算法。这种算法有两个优点：角色划分时考虑了节点间的关系,便于考察节点在整体中的价值；划分时使用的是网络的全局信息,而非网络的静态局部信息,使得分析的视角更为全面。论文以电信通话网络为例进行分析,将用户划分为不同的角色,有利于运营商根据不同角色用户的行为特征制定营销策略。
4.论文提出了一种基于社团结构的二维的PageRank度量方法,两个维度分别称为InnerRank和OutterRank,并给出了根据这两种度量划分网络中的节点角色的算法。InnerRank用于指代节点在社团内部影响力的高低,OutterRank用于指代节点在社团外部影响力的高低。根据这两个影响力度量,可将网络的节点划分成为四种角色。这种角色划分方法对有向网络、无向网络、加权网络、无权网络均可适用。作者通过对现实数据的实验验证了方法的有效性,实验结果表明在上述四种网络中均可根据这种方法分析出节点的角色。
5.论文以在线论坛为例,对在线社会网络数据进行了话题检测分析。通过文本语义分析,在论坛帖子间的语义关系形成后,我们将在线论坛的社会网络数据构建形成时序论坛关系图。本文的研究中,话题检测问题被视为复杂网络分析中的图聚类问题。作者采用复杂网络分析方法对论坛关系图进行聚类,聚类结果即为在线社会网络中的话题。
6.对在线论坛进行话题检测分析后,论文基于信息熵中互信息量的概念,提出了在不同时间间隔下,在线社会网络中话题之间的关联演化分析算法。我们考虑了对应话题的两种影响因素：语义相似性以及社会实体节点的关联性。此外,论文还定义了一个异质论坛关系图结构,该网络包含了论坛中的语义信息和作者间的发表一回复信息。根据社会实体节点对关联话题的演化影响程度,我们对社会个体节点的影响力进行排序,得到了该在线网络的舆论领袖列表。
总体而言,本论文针对复杂社会网络的社会实体节点性质相关的诸多重要方面进行了研究。具体来说,本文的创新性主要体现在如下四个方面：
1.扩展了经典的影响力传播模型,提出了新的节点影响力度量算法。新的度量算法能够有效地提高分析结果的准确率和运行效率。
2.以电信通话网络为例,采用融入结构属性的社会网络分析(SNA)新方法,提出了基于节点结构属性的节点角色划分算法。该算法能够有效地利用网络的全局信息对电信通话网络中的用户角色进行分析。由于电信通话网络是典型的社会网络,该方法也能够推广到一般的社会网络中。
3.提出了一种基于社团结构的二维的PageRank度量方法,两个维度分别称为InnerRank和OutterRank,以及根据这个二维度量以划分网络中的节点角色的算法。根据这两个影响力度量维度,可将网络的节点划分为四种角色。这种新的节点角色划分方法能够适用于有权、无权、有向、无向等各种社会网络。
4.分析了以在线论坛为代表的在线社会网络。对应话题的两种影响因素一语义相似性以及社会实体节点的关联性,我们提出了新的话题关联演化的分析算法,并根据社会实体节点对关联话题的演化影响程度,分析发现了推动舆论演进的重要作者(舆论领袖)。
21 century is the century of complexity. As a powful tool for analyzing the complex science and complex system, complex network technology provides a brand new perspective. Much attention has been paid to the study of complex network by experts in different research fields for its wide range of applications. Hence, complex network analysis has become a hot research topic in recent years.
A complex network is a set of items, which we will call vertices or sometimes nodes, with complex connections between them, called edges. In the recent 10 years, researchers studied deep inside the complex network structure with the beginning of the topological properties. They have proposed many centrality and distance metrics of nodes or edges, and have found that the networks often have the'scale-free' property, the 'small-world'phenomena, as well as the high'clustering-coefficients'.
A social network is the graph of relationships and interactions within a group of social individuals. An online social network is the social network which extracted from the social media data. These two kinds of complex networks have been achieved a lot attentions, and studied extensively in the complex network science. In this paper, we discuss the node influence, node role in social networks, and the correlation of the social entities and topic evolution in online social networks. Analysis of node influence and node role using the social network analysis (SNA) method enables the discovery of the node's function in the network from a microscopic view. Finding the key nodes from the network may receive great practical value and realistic significance. And the analysis of the social media data using the SNA techniques could help us to find the interesting behavior patterns of the Web users, and provide technical support for innovative Web applications.
The main contents in this dissertation can be summarized as follows:
1.We propose a new information diffusion model CTMC-ICM, which introduces the theory of Continuous-Time Markov Chain (CTMC) into the classic Independent Cascade Model (ICM). The novel information diffusion model derived from the basic ICM such that a good estimate ofσ(A) can be efficiently computed, that is, the nodes influenced by a given set A of nodes.
2. We give a new ranking metric named SpreadRank provided by the new information propagation model. SpreadRank is based on random walk theory, and takes into account the node's spreading ability. We introduce a different type of influence metric from the previous random walk based centrality measures by introducing the spreading ability into the transition matrix. We experimentally demonstrate the new ranking method which can in general extract nontrivial nodes as influential node set such that we could maximize the spread of information in social network, and it's more efficient than distance-based centrality.
3. We introduce the structure properties of the network, and combines both of the node identity and the relational data for node role partition. The characteristic of our approach is different from the other behavior-based methods in that it adopts the social network analysis (SNA) methods. By taking into account of the network structure, the role defining process can benefit from two main respects:firstly, it takes account of the interaction data of the nodes which is very useful for understanding the node's value; secondly, it can give a global view instead of a local static view of the data. Finally, the methodology is applied to the telecommunication network. We identify the user role in the operators to help the telecommunication companies formulate strategies according to the users'different behaviors.
4. Two new social influence ranking metrics, InnerRank and OutterRank are proposed based on the concept of modified Pagerank, by considering the community structure knowledge. The InnerRank measures how well connected the node is to other nodes in the community, and the OutterRank is the measure of the links distributed among other communities. We hypothesize that the role of a node can be determined, to a great extent, by its InnerRank degree and OutterRrank degree, which defined how the node is positioned in its own community and with respect to other communities into 4 roles. It is well adapted to direct and weighted networks also. This method is shown to give reasonable results than previous metrics both on synthetic networks and real networks.
5.Take online forums as an example, we analyze the online social media data on the Web. When the semantic links between the forum posts' content are formed, we build the temporal forum graph. In our analysis, the topic detection is in general a graph clustering problem. We cluster the temporal forum graph using the community detection method in social network analysis science, and obtain the results as the topics in the online forums.
6.After the analysis of the topic detection on the forum data, we propose a method for discovering the dependency relationship between the topics of documents in adjacent time stamps based on mutual information measure. The knowledge of content semantic similarity and social interactions of authors and repliers are used to estimate the correlation between the topics. Furthermore, we define a new heterogeneous forum network structure, which include the information of both the semantic relations between the posts and also the publish-reply data between the authors. According to the affection degree of the social entities on the topic correlation evolution, we study the authors'impact and propose a new way for evaluating opinion leaders.
Summarily the distinct feature of this dissertation is the study of methodology for social entities'position in complex social networks. Following are the primary innovativeness of this thesis in four aspects:
1. We propose a new node influence metric, and extend the basic influence diffusion model. The new metric is able to improve the accuracy and efficiency effectively.
2. Taking telecommunication network as an example, we adopt the social network analysis (SNA) methods into node role partition problem. This method is very helpful for understanding the node's value in the network, and gives a global view instead of a static view of the data.
3.Two new social influence ranking metrics, InnerRank and OutterRank are proposed based on the concept of modified Pagerank, and by considering the community structure knowledge, to partition the node role into 4 roles. It can be well adapted to direct and weighted networks.
4. We study the online forums represented for the online social networks, and proposed a new methodology to estimate the correlation between the topics using the knowledge of content semantic similarity and social interactions of authors and repliers. Furthermore, we study the authors' impact according to the affection degree of the social entities on the topic correlation evolution.
复杂网络是指将系统内部的各个元素抽象成为节点,元素之间的关系视为连接的边,而构成的一个具有复杂连接关系的网络。在过去的十年里,学者们对复杂网络结构特性的研究逐步深入,从最早的网络的拓扑性质研究开始,提出了节点或边的中心度量与节点间的距离度量,并揭示出现实世界中的复杂网络所具有的部分独特的统计结构特征,如：“无标度”特性、“小世界”特性以及高聚集系数等。
社会网络系指由社会个体指代节点,个体间的关联作为边形成的网络,在线社会网络系指将互联网的在线社会媒体中抽取出的实体作为节点,实体间的关联作为边形成的网络,这两类网络的研究在复杂网络研究领域获得了很多学者的关注。本论文主要对社会网络的节点影响力、节点角色,以及在线社会网络研究中话题和社会节点关联演化等问题进行了研究和探讨。分析社会网络中的节点影响力以及节点角色,进而找出社会网络中的关键节点具有很大的实用价值和现实意义。使用社会网络分析技术对在线社会媒体数据进行分析,能够发现网络用户的各种行为模式,为更多创新性的Web应用提供支持。
本论文的主要研究内容如下：
1.提出了新的影响力传播模型。论文将连续时间马尔可夫链(Continuous-Time Markov Chain, CTMC)引入经典的独立级联模型([CM),从而给出了一个改进的ICM模型,本文称之为CTMC-ICM模型。通过该模型可以得到对σ(A)的较精确的估计值,即在给定节点集合A时,能够被集合A影响到的节点数量的估计
2.基于随机游走的理论,考虑到不同节点可具有不同的传播能力,本文提出了一个新的节点影响力度量算法,称为SpreadRank度量算法。不同于过去的基于随机游走理论的中心度度量,SpreadRank将节点的传播能力引入转移矩阵函数。实验结果表明它比基于距离的中心度量方法效率更高,并且新的排序算法能够从所有节点中提取出具有影响力的节点集,通过激活这个影响力节点集合,可以将信息最大化地在网络中进行传播。
3.论文对节点的角色划分进行了分析,结合了复杂网络结构的关系属性以及节点个体属性信息,提出了一种融入结构属性的新的社会网络分析(SNA)算法。这种算法有两个优点：角色划分时考虑了节点间的关系,便于考察节点在整体中的价值；划分时使用的是网络的全局信息,而非网络的静态局部信息,使得分析的视角更为全面。论文以电信通话网络为例进行分析,将用户划分为不同的角色,有利于运营商根据不同角色用户的行为特征制定营销策略。
4.论文提出了一种基于社团结构的二维的PageRank度量方法,两个维度分别称为InnerRank和OutterRank,并给出了根据这两种度量划分网络中的节点角色的算法。InnerRank用于指代节点在社团内部影响力的高低,OutterRank用于指代节点在社团外部影响力的高低。根据这两个影响力度量,可将网络的节点划分成为四种角色。这种角色划分方法对有向网络、无向网络、加权网络、无权网络均可适用。作者通过对现实数据的实验验证了方法的有效性,实验结果表明在上述四种网络中均可根据这种方法分析出节点的角色。
5.论文以在线论坛为例,对在线社会网络数据进行了话题检测分析。通过文本语义分析,在论坛帖子间的语义关系形成后,我们将在线论坛的社会网络数据构建形成时序论坛关系图。本文的研究中,话题检测问题被视为复杂网络分析中的图聚类问题。作者采用复杂网络分析方法对论坛关系图进行聚类,聚类结果即为在线社会网络中的话题。
6.对在线论坛进行话题检测分析后,论文基于信息熵中互信息量的概念,提出了在不同时间间隔下,在线社会网络中话题之间的关联演化分析算法。我们考虑了对应话题的两种影响因素：语义相似性以及社会实体节点的关联性。此外,论文还定义了一个异质论坛关系图结构,该网络包含了论坛中的语义信息和作者间的发表一回复信息。根据社会实体节点对关联话题的演化影响程度,我们对社会个体节点的影响力进行排序,得到了该在线网络的舆论领袖列表。
总体而言,本论文针对复杂社会网络的社会实体节点性质相关的诸多重要方面进行了研究。具体来说,本文的创新性主要体现在如下四个方面：
1.扩展了经典的影响力传播模型,提出了新的节点影响力度量算法。新的度量算法能够有效地提高分析结果的准确率和运行效率。
2.以电信通话网络为例,采用融入结构属性的社会网络分析(SNA)新方法,提出了基于节点结构属性的节点角色划分算法。该算法能够有效地利用网络的全局信息对电信通话网络中的用户角色进行分析。由于电信通话网络是典型的社会网络,该方法也能够推广到一般的社会网络中。
3.提出了一种基于社团结构的二维的PageRank度量方法,两个维度分别称为InnerRank和OutterRank,以及根据这个二维度量以划分网络中的节点角色的算法。根据这两个影响力度量维度,可将网络的节点划分为四种角色。这种新的节点角色划分方法能够适用于有权、无权、有向、无向等各种社会网络。
4.分析了以在线论坛为代表的在线社会网络。对应话题的两种影响因素一语义相似性以及社会实体节点的关联性,我们提出了新的话题关联演化的分析算法,并根据社会实体节点对关联话题的演化影响程度,分析发现了推动舆论演进的重要作者(舆论领袖)。
21 century is the century of complexity. As a powful tool for analyzing the complex science and complex system, complex network technology provides a brand new perspective. Much attention has been paid to the study of complex network by experts in different research fields for its wide range of applications. Hence, complex network analysis has become a hot research topic in recent years.
A complex network is a set of items, which we will call vertices or sometimes nodes, with complex connections between them, called edges. In the recent 10 years, researchers studied deep inside the complex network structure with the beginning of the topological properties. They have proposed many centrality and distance metrics of nodes or edges, and have found that the networks often have the'scale-free' property, the 'small-world'phenomena, as well as the high'clustering-coefficients'.
A social network is the graph of relationships and interactions within a group of social individuals. An online social network is the social network which extracted from the social media data. These two kinds of complex networks have been achieved a lot attentions, and studied extensively in the complex network science. In this paper, we discuss the node influence, node role in social networks, and the correlation of the social entities and topic evolution in online social networks. Analysis of node influence and node role using the social network analysis (SNA) method enables the discovery of the node's function in the network from a microscopic view. Finding the key nodes from the network may receive great practical value and realistic significance. And the analysis of the social media data using the SNA techniques could help us to find the interesting behavior patterns of the Web users, and provide technical support for innovative Web applications.
The main contents in this dissertation can be summarized as follows:
1.We propose a new information diffusion model CTMC-ICM, which introduces the theory of Continuous-Time Markov Chain (CTMC) into the classic Independent Cascade Model (ICM). The novel information diffusion model derived from the basic ICM such that a good estimate ofσ(A) can be efficiently computed, that is, the nodes influenced by a given set A of nodes.
2. We give a new ranking metric named SpreadRank provided by the new information propagation model. SpreadRank is based on random walk theory, and takes into account the node's spreading ability. We introduce a different type of influence metric from the previous random walk based centrality measures by introducing the spreading ability into the transition matrix. We experimentally demonstrate the new ranking method which can in general extract nontrivial nodes as influential node set such that we could maximize the spread of information in social network, and it's more efficient than distance-based centrality.
3. We introduce the structure properties of the network, and combines both of the node identity and the relational data for node role partition. The characteristic of our approach is different from the other behavior-based methods in that it adopts the social network analysis (SNA) methods. By taking into account of the network structure, the role defining process can benefit from two main respects:firstly, it takes account of the interaction data of the nodes which is very useful for understanding the node's value; secondly, it can give a global view instead of a local static view of the data. Finally, the methodology is applied to the telecommunication network. We identify the user role in the operators to help the telecommunication companies formulate strategies according to the users'different behaviors.
4. Two new social influence ranking metrics, InnerRank and OutterRank are proposed based on the concept of modified Pagerank, by considering the community structure knowledge. The InnerRank measures how well connected the node is to other nodes in the community, and the OutterRank is the measure of the links distributed among other communities. We hypothesize that the role of a node can be determined, to a great extent, by its InnerRank degree and OutterRrank degree, which defined how the node is positioned in its own community and with respect to other communities into 4 roles. It is well adapted to direct and weighted networks also. This method is shown to give reasonable results than previous metrics both on synthetic networks and real networks.
5.Take online forums as an example, we analyze the online social media data on the Web. When the semantic links between the forum posts' content are formed, we build the temporal forum graph. In our analysis, the topic detection is in general a graph clustering problem. We cluster the temporal forum graph using the community detection method in social network analysis science, and obtain the results as the topics in the online forums.
6.After the analysis of the topic detection on the forum data, we propose a method for discovering the dependency relationship between the topics of documents in adjacent time stamps based on mutual information measure. The knowledge of content semantic similarity and social interactions of authors and repliers are used to estimate the correlation between the topics. Furthermore, we define a new heterogeneous forum network structure, which include the information of both the semantic relations between the posts and also the publish-reply data between the authors. According to the affection degree of the social entities on the topic correlation evolution, we study the authors'impact and propose a new way for evaluating opinion leaders.
Summarily the distinct feature of this dissertation is the study of methodology for social entities'position in complex social networks. Following are the primary innovativeness of this thesis in four aspects:
1. We propose a new node influence metric, and extend the basic influence diffusion model. The new metric is able to improve the accuracy and efficiency effectively.
2. Taking telecommunication network as an example, we adopt the social network analysis (SNA) methods into node role partition problem. This method is very helpful for understanding the node's value in the network, and gives a global view instead of a static view of the data.
3.Two new social influence ranking metrics, InnerRank and OutterRank are proposed based on the concept of modified Pagerank, and by considering the community structure knowledge, to partition the node role into 4 roles. It can be well adapted to direct and weighted networks.
4. We study the online forums represented for the online social networks, and proposed a new methodology to estimate the correlation between the topics using the knowledge of content semantic similarity and social interactions of authors and repliers. Furthermore, we study the authors' impact according to the affection degree of the social entities on the topic correlation evolution.
引文
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