CN109034562A - A kind of social networks node importance appraisal procedure and system - Google Patents
A kind of social networks node importance appraisal procedure and system Download PDFInfo
- Publication number
- CN109034562A CN109034562A CN201810744899.6A CN201810744899A CN109034562A CN 109034562 A CN109034562 A CN 109034562A CN 201810744899 A CN201810744899 A CN 201810744899A CN 109034562 A CN109034562 A CN 109034562A
- Authority
- CN
- China
- Prior art keywords
- node
- importance
- index value
- nodes
- evaluated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明涉及一种社交网络节点重要性评估方法及系统,属于社交网络分析技术领域,解决了现有技术中基于H指数或类H指数的节点重要性评估方法对节点重要性区分度不高、无法有效对具有相同H值的节点进行重要性排序的问题。包括以下步骤:求取给定社交网络中节点的K指数值;根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度;基于待评估节点的重要度对该待评估节点的重要性进行评估。本发明充分利用了邻居节点的影响力,可以对相同H指数值的节点重要性进行有效区分排序,能够快速、准确地对社交网络中的节点重要性进行评估,并且评估结果区分度高;同时,能对大规模社交网络进行分析,便于快速发现重要性节点,适应性强。
The invention relates to a social network node importance evaluation method and system, which belongs to the technical field of social network analysis, and solves the problem that the node importance evaluation method based on the H-index or H-like index in the prior art has a low degree of differentiation of node importance, The problem of not being able to effectively sort the importance of nodes with the same H value. The method includes the following steps: obtaining the K index value of a node in a given social network; determining the importance of the node to be evaluated according to the sum of the K index values of all neighbor nodes of the node to be evaluated; determining the importance of the node to be evaluated based on the importance of the node to be evaluated The importance of evaluation nodes is evaluated. The invention makes full use of the influence of neighbor nodes, can effectively distinguish and sort the importance of nodes with the same H index value, can quickly and accurately evaluate the importance of nodes in a social network, and the evaluation results have a high degree of discrimination; at the same time , which can analyze large-scale social networks, facilitate the rapid discovery of important nodes, and has strong adaptability.
Description
技术领域technical field
本发明涉及社交网络分析技术领域,尤其涉及一种社交网络节点重要性评估方法及系统。The invention relates to the technical field of social network analysis, in particular to a method and system for evaluating the importance of social network nodes.
背景技术Background technique
近年来,以微信、微博、FaceBook、Linkedin为代表社交软件的繁荣极大地促进了社交网络研究的发展,其中社交网络节点重要性评估是社交网络研究的一个重要方向,快速有效地对网络节点进行重要性评估对进一步识别关键节点、分析网络结构有重要意义。In recent years, the prosperity of social software represented by WeChat, Weibo, FaceBook, and Linkedin has greatly promoted the development of social network research, among which the importance evaluation of social network nodes is an important direction of social network research. Importance assessment is of great significance for further identifying key nodes and analyzing network structure.
现有的社交网络中节点重要性度量方法根据实现方法,可划分为基于网络局部属性、基于网络全局属性、基于随机游走以及基于社团结构的方法。其中,基于局部属性信息的代表性方法为度中心性方法,节点的度值表示与该节点相连的节点个数,可以直观地反映节点的局部重要性,却无法很好地体现该节点在整个网络中的情况;基于全局属性的度量包括介数中心性、紧密度中心性、特征向量中心性等,这类方法的时间复杂度相对较高,不适合大型网络。Existing node importance measurement methods in social networks can be divided into methods based on network local attributes, network global attributes, random walks, and community structures according to the implementation methods. Among them, the representative method based on local attribute information is the degree centrality method. The degree value of a node indicates the number of nodes connected to the node, which can intuitively reflect the local importance of the node, but cannot well reflect the node's importance in the whole The situation in the network; measures based on global attributes include betweenness centrality, compactness centrality, eigenvector centrality, etc. The time complexity of such methods is relatively high, and they are not suitable for large networks.
H指数最初用来评价研究人员的个人成就影响力,将H指数对应于社交网络中的节点,如果一个节点至少有h个邻居节点的度为h,则该节点的H指数值为h。然而,直接将H指数应用于社交网络节点重要性评估会出现和k-shell分解算法一样的缺陷,同一h值的节点无法区分开。导致这一缺陷的根本原因在于社交网络中节点的重要性不仅取决于其自身的度量值,还取决于其邻居节点对该节点的影响力,或者邻居节点对该节点的依赖程度。为解决此问题,出现了如g指数、K指数、w指数等类H指数。其中,K指数通过邻居节点的总度数之和进行进一步细分,但仍然无法对部分节点进行有效区分。The H index was originally used to evaluate the influence of researchers' personal achievements. The H index corresponds to the nodes in the social network. If a node has at least h neighbor nodes with a degree of h, then the node's H index value is h. However, directly applying the H index to the evaluation of the importance of social network nodes will have the same flaws as the k-shell decomposition algorithm, and nodes with the same h value cannot be distinguished. The root cause of this defect is that the importance of a node in a social network not only depends on its own metric value, but also depends on the influence of its neighbors on the node, or the degree of dependence of the neighbors on the node. To solve this problem, H-like indices such as g-index, K-index, and w-index have emerged. Among them, the K index is further subdivided by the sum of the total degrees of neighbor nodes, but it still cannot effectively distinguish some nodes.
发明内容Contents of the invention
鉴于上述的分析,本发明旨在提供一种社交网络节点重要性评估方法及系统,用以解决现有基于H指数或类H指数的节点重要性评估方法对节点重要性区分度不高、无法有效对具有相同H值的节点的进行重要性排序的问题。In view of the above analysis, the present invention aims to provide a social network node importance evaluation method and system to solve the problem that the existing node importance evaluation methods based on H-index or H-like index have low degree of distinction of node importance and cannot The problem of efficiently ranking the importance of nodes with the same H value.
本发明的目的主要是通过以下技术方案实现的:The purpose of the present invention is mainly achieved through the following technical solutions:
一方面,提供了一种社交网络节点重要性评估方法,包括以下步骤:On the one hand, a method for assessing the importance of social network nodes is provided, including the following steps:
求取给定社交网络中节点的K指数值;Find the K-index value of a node in a given social network;
根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度;Determine the importance of the node to be evaluated according to the sum of the K index values of all neighbor nodes of the node to be evaluated;
基于待评估节点的重要度对该待评估节点的重要性进行评估。The importance of the node to be evaluated is evaluated based on the importance of the node to be evaluated.
本发明有益效果如下:本发明充分利用社交网络节点间的连接信息,综合考虑邻居节点的影响力,不仅利用了节点本身的影响力,还充分利用了邻居节点的影响力,可以对相同H指数值的节点重要性进行有效区分,能够快速、准确地对社交网络中的节点重要性进行评估,同时评估结果区分度高,能对大规模社交网络进行分析,便于快速发现重要性节点,适应性强。The beneficial effects of the present invention are as follows: the present invention makes full use of the connection information between social network nodes, comprehensively considers the influence of neighbor nodes, not only utilizes the influence of the node itself, but also makes full use of the influence of neighbor nodes, and can make full use of the same H index It can effectively distinguish the importance of nodes in the social network, and can quickly and accurately evaluate the importance of nodes in the social network. At the same time, the evaluation results are highly differentiated, and it can analyze large-scale social networks, which is convenient for quickly discovering important nodes. Adaptability powerful.
在上述方案的基础上,本发明还做了如下改进:On the basis of the foregoing scheme, the present invention has also made the following improvements:
进一步,所述求取社交网络中节点的K指数值,包括以下步骤:Further, said obtaining the K index value of the node in the social network includes the following steps:
求取给定社交网络中节点的H指数值;Find the H-index value of a node in a given social network;
在上述节点的邻居节点集合中选取度值不小于该节点H指数值的邻居节点;Select a neighbor node whose degree value is not less than the H index value of the node in the neighbor node set of the above node;
根据选取出的邻居节点的度值之和,确定该节点的K指数值。According to the sum of the degree values of the selected neighbor nodes, the K index value of the node is determined.
进一步,所述根据选取出的邻居节点的度值之和,确定该节点的K指数值,公式为:Further, the K index value of the node is determined according to the sum of the degree values of the selected neighbor nodes, the formula is:
式中,Ki表示社交网络中节点i的K指数值,hi表示节点i的H指数值,表示选取出的邻居节点的度值之和。In the formula, K i represents the K index value of node i in the social network, h i represents the H index value of node i, Indicates the sum of the degree values of the selected neighbor nodes.
进一步,根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度,公式为:Further, according to the sum of K index values of all neighbor nodes of the node to be evaluated, the importance of the node to be evaluated is determined, and the formula is:
式中,Γ(i)指待评估节点i的邻居节点集合,Kj为邻居节点j的K指数值,LK(i)为待评估节点i的重要度。In the formula, Γ (i) refers to the set of neighbor nodes of node i to be evaluated, K j is the K index value of neighbor node j, and LK (i) is the importance of node i to be evaluated.
进一步,所述求取给定社交网络中节点的H指数值,包括以下步骤:Further, said obtaining the H-index value of a node in a given social network includes the following steps:
求取社交网络中所有节点的度值;Find the degree value of all nodes in the social network;
采用二分查找法计算社交网络中节点的H指数值。The binary search method is used to calculate the H index value of the nodes in the social network.
另一方面,还提供了一种社交网络节点重要性评估系统,包括:On the other hand, a social network node importance evaluation system is also provided, including:
节点K指数值求取模块,用于求取给定社交网络中节点的K指数值;The node K index value calculation module is used to obtain the K index value of the node in the given social network;
节点重要度确定模块,用于根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度,并输出给节点重要性评估模块;The node importance determination module is used to determine the importance of the node to be evaluated according to the sum of the K index values of all neighbor nodes of the node to be evaluated, and output to the node importance evaluation module;
节点重要性评估模块,通过接收到的节点的重要度对该待评估节点的重要性进行评估。The node importance evaluation module evaluates the importance of the node to be evaluated according to the received node importance.
本发明有益效果如下:本发明充分利用社交网络节点间的连接信息,综合考虑邻居节点的影响力,不仅利用了节点本身的影响力,还充分利用了邻居节点的影响力,可以对相同H指数值的节点重要性进行有效区分,能够快速、准确地对社交网络中的节点重要性进行评估,同时评估结果区分度高,能对大规模社交网络进行分析,便于快速发现重要性节点,适应性强。The beneficial effects of the present invention are as follows: the present invention makes full use of the connection information between social network nodes, comprehensively considers the influence of neighbor nodes, not only utilizes the influence of the node itself, but also makes full use of the influence of neighbor nodes, and can make full use of the same H index It can effectively distinguish the importance of nodes in the social network, and can quickly and accurately evaluate the importance of nodes in the social network. At the same time, the evaluation results are highly differentiated, and it can analyze large-scale social networks, which is convenient for quickly discovering important nodes. Adaptability powerful.
在上述方案的基础上,本发明还做了如下改进:On the basis of the foregoing scheme, the present invention has also made the following improvements:
进一步,所述节点K指数值求取模块,包括H指数值求取单元、邻居节点选取单元、K指数值确定单元;Further, the node K index value calculation module includes an H index value calculation unit, a neighbor node selection unit, and a K index value determination unit;
所述H指数值求取单元,用于求取给定社交网络中节点的H指数值,并输出给邻居节点选取单元;The H-index value calculating unit is used to calculate the H-index value of a node in a given social network, and output it to a neighbor node selection unit;
所述邻居节点选取单元,在上述节点的邻居节点集合中选取度值不小于该节点H指数值的邻居节点;The neighbor node selection unit selects a neighbor node whose degree value is not less than the H index value of the node in the neighbor node set of the above-mentioned node;
所述K指数值确定单元,根据所述邻居节点选取单元选取出的邻居节点的度值之和,确定该节点的K指数值。The K index value determination unit determines the K index value of the node according to the sum of the degree values of the neighbor nodes selected by the neighbor node selection unit.
进一步,根据选取出的邻居节点的度值之和,确定该节点的K指数值,公式为:Further, according to the sum of the degree values of the selected neighbor nodes, determine the K index value of the node, the formula is:
式中,Ki表示社交网络中节点i的K指数值,hi表示节点i的H指数值,表示选取出的邻居节点的度值之和。In the formula, K i represents the K index value of node i in the social network, h i represents the H index value of node i, Indicates the sum of the degree values of the selected neighbor nodes.
进一步,根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度,公式为:Further, according to the sum of K index values of all neighbor nodes of the node to be evaluated, the importance of the node to be evaluated is determined, and the formula is:
式中,Γ(i)为待评估节点i的邻居节点集合,Kj为邻居节点j的K指数值,LK(i)为待评估节点i的重要度。In the formula, Γ (i) is the set of neighbor nodes of node i to be evaluated, K j is the K index value of neighbor node j, and LK (i) is the importance of node i to be evaluated.
进一步,所述节点H指数值求取单元包括度值求取子单元、H指数值求取子单元:Further, the node H index value calculation unit includes a degree value calculation subunit and an H index value calculation subunit:
所述度值求取子单元用于求取社交网络中所有节点的度值,并输出给所述H指数值求取子单元;The degree value calculation subunit is used to calculate the degree values of all nodes in the social network, and output to the H index value calculation subunit;
所述H指数值求取子单元用于采用二分查找法计算社交网络中节点的H指数值。The H-index calculation subunit is used to calculate the H-index value of the nodes in the social network by using the binary search method.
本发明中,上述各技术方案之间还可以相互组合,以实现更多的优选组合方案。本发明的其他特征和优点将在随后的说明书中阐述,并且,部分优点可从说明书中变得显而易见,或者通过实施本发明而了解。本发明的目的和其他优点可通过说明书、权利要求书以及附图中所特别指出的内容中来实现和获得。In the present invention, the above technical solutions can also be combined with each other to realize more preferred combination solutions. Additional features and advantages of the invention will be set forth in the description which follows, and some of the advantages will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by what is particularly pointed out in the written description, claims as well as the appended drawings.
附图说明Description of drawings
附图仅用于示出具体实施例的目的,而并不认为是对本发明的限制,在整个附图中,相同的参考符号表示相同的部件。The drawings are for the purpose of illustrating specific embodiments only and are not to be considered as limitations of the invention, and like reference numerals refer to like parts throughout the drawings.
图1为本发明实施例一中所述的社交网络节点重要性评估方法的流程图。FIG. 1 is a flow chart of the method for evaluating the importance of social network nodes described in Embodiment 1 of the present invention.
图2为本发明实施例二中所述的社交网络节点重要性评估系统的结构示意图。FIG. 2 is a schematic structural diagram of the social network node importance evaluation system described in Embodiment 2 of the present invention.
图3为本发明实施例三和四中所述的简单示例网络的拓扑结构示意图。FIG. 3 is a schematic diagram of a topology structure of a simple example network described in Embodiments 3 and 4 of the present invention.
图4为图3所示网络的互补累积分布函数CCDF示意图。FIG. 4 is a schematic diagram of the complementary cumulative distribution function CCDF of the network shown in FIG. 3 .
图5为本发明实施例四中所述Karate club网络的互补累积分布函数CCDF示意图。FIG. 5 is a schematic diagram of the complementary cumulative distribution function CCDF of the Karate club network described in Embodiment 4 of the present invention.
图6为本发明实施例四中所述Dolphin网络的互补累积分布函数CCDF示意图。FIG. 6 is a schematic diagram of the complementary cumulative distribution function CCDF of the Dolphin network described in Embodiment 4 of the present invention.
图7为本发明实施例四中所述Celegan网络的互补累积分布函数CCDF示意图。FIG. 7 is a schematic diagram of the complementary cumulative distribution function CCDF of the Celegan network described in Embodiment 4 of the present invention.
图8为本发明实施例四中所述LFR网络生成器的n参数变化时不同节点重要性评估方法的区分度指标变化示意图。FIG. 8 is a schematic diagram of changes in the discrimination index of different node importance evaluation methods when the n parameter of the LFR network generator described in Embodiment 4 of the present invention changes.
图9为本发明实施例四中所述LFR网络生成器的μ参数变化时不同节点重要性评估方法的区分度指标变化示意图。FIG. 9 is a schematic diagram of changes in the discrimination index of different node importance evaluation methods when the μ parameter of the LFR network generator described in Embodiment 4 of the present invention changes.
图10为本发明实施例四中所述LFR网络生成器的k参数变化时不同节点重要性评估方法的区分度指标变化示意图。FIG. 10 is a schematic diagram of changes in the discrimination index of different node importance evaluation methods when the k parameter of the LFR network generator described in Embodiment 4 of the present invention changes.
图11为本发明实施例四中所述LFR网络生成器的λ参数变化时不同节点重要性评估方法的区分度指标变化示意图。FIG. 11 is a schematic diagram of the variation of the discrimination index of different node importance evaluation methods when the λ parameter of the LFR network generator described in Embodiment 4 of the present invention changes.
具体实施方式Detailed ways
下面结合附图来具体描述本发明的优选实施例,其中,附图构成本申请一部分,并与本发明的实施例一起用于阐释本发明的原理,并非用于限定本发明的范围。Preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the application and together with the embodiments of the present invention are used to explain the principle of the present invention and are not intended to limit the scope of the present invention.
实施例一Embodiment one
公开了一种社交网络节点重要性评估方法。如图1所示,包括以下步骤:A social network node importance evaluation method is disclosed. As shown in Figure 1, the following steps are included:
步骤S1、求取给定社交网络中节点的K指数值;Step S1, obtain the K index value of the node in the given social network;
步骤S2、根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度;Step S2. Determine the importance of the node to be evaluated according to the sum of the K index values of all neighboring nodes of the node to be evaluated;
步骤S3、基于待评估节点的重要度对该待评估节点的重要性进行评估。Step S3, evaluating the importance of the node to be evaluated based on the importance of the node to be evaluated.
与现有技术相比,本实施例提供的社交网络节点重要性评估方法,充分利用社交网络节点间的连接信息,综合考虑邻居节点的影响力,不仅利用了节点本身的影响力,还充分利用了节点邻居节点的影响力,可以对相同H指数值的节点重要性进行有效区分,能够快速、准确地对社交网络中的节点重要性进行评估,同时评估结果区分度高,能对大规模社交网络进行分析,便于快速发现重要性节点,适应性强。Compared with the prior art, the social network node importance evaluation method provided by this embodiment makes full use of the connection information between social network nodes and comprehensively considers the influence of neighbor nodes, not only using the influence of the node itself, but also making full use of It can effectively distinguish the importance of nodes with the same H index value, and can quickly and accurately evaluate the importance of nodes in social networks. At the same time, the evaluation results are highly differentiated, and can be used for large-scale social Network analysis facilitates quick discovery of important nodes and strong adaptability.
具体来说,步骤S1中,求取社交网络中节点的K指数值,包括以下步骤:Specifically, in step S1, calculating the K index value of the nodes in the social network includes the following steps:
步骤S101,求取给定社交网络中节点的H指数值;Step S101, obtaining the H-index value of a node in a given social network;
首先求取给定社交网络中所有节点的度值;然后依据H指数的定义,根据求取的度值,计算社交网络中节点的H指数值h(节点的H指数值h说明该节点的至少h个邻居节点的度都大于h)。优选的,采用二次查找法计算H指数值h。First calculate the degree value of all nodes in a given social network; then according to the definition of the H index, calculate the H index value h of the node in the social network according to the obtained degree value (the H index value h of the node indicates that the node has at least The degrees of h neighboring nodes are all greater than h). Preferably, the H-index value h is calculated using a quadratic search method.
步骤S102,在节点的邻居节点集合中选取度值不小于该节点H指数值的邻居节点;Step S102, selecting a neighbor node whose degree value is not less than the H index value of the node in the neighbor node set of the node;
步骤S103,根据步骤S102中选取出的邻居节点的度值之和,确定该节点的K指数值。Step S103, according to the sum of the degree values of the neighbor nodes selected in step S102, determine the K index value of the node.
其中,节点的K指数值计算公式为:Among them, the formula for calculating the K index value of a node is:
式中,Ki表示社交网络中节点i的K指数值,hi表示节点i的H指数值,表示选取出的邻居节点的度值之和。In the formula, K i represents the K index value of node i in the social network, h i represents the H index value of node i, Indicates the sum of the degree values of the selected neighbor nodes.
在步骤S2中,根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度,公式为:In step S2, the importance of the node to be evaluated is determined according to the sum of the K index values of all neighbor nodes of the node to be evaluated, the formula is:
式中,Γ(i)指待评估节点i的邻居节点集合,Kj为邻居节点j的K指数值,LK(i)为待评估节点i的重要度。In the formula, Γ (i) refers to the set of neighbor nodes of node i to be evaluated, K j is the K index value of neighbor node j, and LK (i) is the importance of node i to be evaluated.
在步骤S3中,基于确定的节点的重要度对该节点的重要性进行评估。在得到社交网络中所有节点的重要度之后,将所有节点按照重要度进行排序,进而评估节点的重要性(重要度数值越大,重要性越高)。In step S3, the importance of the node is evaluated based on the determined importance of the node. After obtaining the importance of all nodes in the social network, all nodes are sorted according to the importance, and then the importance of the node is evaluated (the greater the value of the importance, the higher the importance).
实施例二Embodiment two
公开了一种社交网络节点重要性评估系统。如图2所示,包括:节点K指数值求取模块、节点重要度确定模块、节点重要性评估模块;其中,A social network node importance evaluation system is disclosed. As shown in Figure 2, it includes: a node K index value calculation module, a node importance determination module, and a node importance evaluation module; wherein,
节点K指数值求取模块,用于求取给定社交网络中节点的K指数值;The node K index value calculation module is used to obtain the K index value of the node in the given social network;
节点重要度确定模块,用于根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度,并输出给节点重要性评估模块;The node importance determination module is used to determine the importance of the node to be evaluated according to the sum of the K index values of all neighbor nodes of the node to be evaluated, and output to the node importance evaluation module;
节点重要性评估模块,通过接收到的节点的重要度对该待评估节点的重要性进行评估。The node importance evaluation module evaluates the importance of the node to be evaluated according to the received node importance.
与现有技术相比,本实施例提供的社交网络节点重要性评估系统,充分利用社交网络节点间的连接信息,综合考虑邻居节点的影响力,不仅利用了节点本身的影响力,还充分利用了邻居节点的影响力,可以对相同H指数值的节点重要性进行有效区分,能够快速、准确地对社交网络中的节点重要性进行评估,同时评估结果区分度高,能对大规模社交网络进行分析,便于快速发现重要性节点,适应性强。Compared with the prior art, the social network node importance evaluation system provided by this embodiment makes full use of the connection information between social network nodes and comprehensively considers the influence of neighbor nodes, not only using the influence of the node itself, but also making full use of It can effectively distinguish the importance of nodes with the same H index value, and can quickly and accurately evaluate the importance of nodes in social networks. At the same time, the evaluation results are highly differentiated, and can be used for large-scale social networks. It is easy to quickly find important nodes through analysis, and has strong adaptability.
具体来说,节点K指数值求取模块,包括H指数值求取单元、邻居节点选取单元、K指数值确定单元;其中,Specifically, the node K index value calculation module includes an H index value calculation unit, a neighbor node selection unit, and a K index value determination unit; wherein,
H指数值求取单元,用于求取给定社交网络中节点的H指数值,并输出给邻居节点选取单元;The H index value calculation unit is used to obtain the H index value of the node in the given social network, and output to the neighbor node selection unit;
需要说明的是,H指数值求取单元包括度值求取子单元、H指数值求取子单元:度值求取子单元用于求取社交网络中所有节点的度值,并输出给H指数值求取子单元;H指数值求取子单元用于采用二分查找法计算社交网络中节点的H指数值。It should be noted that the H-index value calculation unit includes a degree value calculation subunit and an H-index value calculation subunit: the degree value calculation subunit is used to calculate the degree values of all nodes in the social network and output them to H The subunit for calculating the index value; the subunit for calculating the H index value is used to calculate the H index value of the node in the social network by using the binary search method.
邻居节点选取单元,在上述节点的邻居节点集合中选取度值不小于该节点H指数值的邻居节点;The neighbor node selection unit selects the neighbor node whose degree value is not less than the H index value of the node in the neighbor node set of the above-mentioned node;
K指数值确定单元,根据邻居节点选取单元选取出的邻居节点的度值之和,确定该节点的K指数值。The K index value determination unit determines the K index value of the node according to the sum of the degree values of the neighbor nodes selected by the neighbor node selection unit.
需要强调的是,在K指数值确定单元中,节点的K指数值计算公式为:It should be emphasized that in the K-index value determination unit, the formula for calculating the K-index value of a node is:
式中,Ki表示社交网络中节点i的K指数值,hi表示节点i的H指数值,表示选取出的邻居节点的度值之和。In the formula, K i represents the K index value of node i in the social network, h i represents the H index value of node i, Indicates the sum of the degree values of the selected neighbor nodes.
节点重要度确定模块,用于确定待评估节点的重要度,并输出给节点重要性评估模块;具体地,根据待评估节点的所有邻居节点的K指数值之和,确定待评估节点的重要度,公式为:The node importance determination module is used to determine the importance of the node to be evaluated, and output to the node importance evaluation module; specifically, according to the sum of the K index values of all neighbor nodes of the node to be evaluated, determine the importance of the node to be evaluated , the formula is:
式中,Γ(i)指待评估节点i的邻居节点集合,Kj为邻居节点j的K指数值,LK(i)为待评估节点i的重要度。In the formula, Γ (i) refers to the set of neighbor nodes of node i to be evaluated, K j is the K index value of neighbor node j, and LK (i) is the importance of node i to be evaluated.
节点重要性评估模块,用于获取节点重要度确定模块的节点的重要度,并对该节点的重要性进行评估。在获得社交网络中所有节点的重要度之后,将所有节点按照重要度进行排序,进而评估节点的重要性(重要度数值越大,重要性越高)。The node importance evaluation module is used to obtain the importance degree of the node of the node importance degree determination module, and evaluate the importance of the node. After obtaining the importance of all nodes in the social network, all nodes are sorted according to the importance, and then the importance of the node is evaluated (the greater the value of the importance, the higher the importance).
实施例三Embodiment Three
本实施例以一个简单示例网络为例,将实施例一中社交网络节点重要性评估方法用于评估该网络节点的重要性。示例网络的拓扑结构如图3所示,包含17个节点和21条边。具体包括以下步骤:In this embodiment, a simple example network is taken as an example, and the social network node importance evaluation method in Embodiment 1 is used to evaluate the importance of the network node. The topology of the example network is shown in Figure 3, which contains 17 nodes and 21 edges. Specifically include the following steps:
1)对给定的示例网络,依据H指数的定义采用二分查找法计算社交网络中节点的H指数值见表1。1) For a given example network, according to the definition of H-index, the binary search method is used to calculate the H-index value of nodes in the social network, as shown in Table 1.
表1:示例网络节点的H指数值Table 1: H-index values for example network nodes
2)根据在节点的邻居节点集合中选取度值不小于该节点H指数值的邻居节点;并计算选取出的邻居节点的度值之和,得到该节点的K指数值。示例网络结点的K指数值见表2。2) Select the neighbor node whose degree value is not less than the H index value of the node in the neighbor node set of the node; and calculate the sum of the degree values of the selected neighbor nodes to obtain the K index value of the node. See Table 2 for the K-index values of example network nodes.
表2:示例网络节点的K指数值Table 2: K-index values for example network nodes
3)计算节点的所有邻居节点的K指数值之和作为该节点的重要度。按照本发明实施例一中所述的节点重要度计算公式,计算出的示例网络节点的重要度见表3。3) Calculate the sum of the K index values of all neighbor nodes of the node as the importance of the node. According to the node importance calculation formula described in Embodiment 1 of the present invention, the calculated importance of an example network node is shown in Table 3.
表3:示例网络节点的重要度Table 3: Importance of example network nodes
由表3可见,几乎所有的示例网络节点都被赋予了不同的重要度。很好地将示例网络节点的重要度区分开,进而使得重要性评估结果区分度更高。It can be seen from Table 3 that almost all example network nodes are given different degrees of importance. The importance of the example network nodes is well distinguished, which in turn makes the importance evaluation results more differentiated.
实施例四Embodiment four
本实施例以真实网络和人工网络为例,将实施例一中所述社交网络节点重要性评估方法用于上述网络的节点重要性评估,并与其他现有节点重要性评估方法进行比较。选取的典型方法包括:H指数(简称H),K指数(简称K),PageRank算法(简称PR),经典K核分解算法(简称KS),KS-IF算法(简称KSIF),MDD算法(简称MDD),本发明方法(简称LK)。为了更好地评价各种重要性评估方法的性能,此处引入区分度指标M。区分度指标定义如下:In this embodiment, real networks and artificial networks are taken as examples, and the social network node importance evaluation method described in Embodiment 1 is used for node importance evaluation of the above network, and compared with other existing node importance evaluation methods. Typical methods selected include: H index (abbreviated as H), K index (abbreviated as K), PageRank algorithm (abbreviated as PR), classic K kernel decomposition algorithm (abbreviated as KS), KS-IF algorithm (abbreviated as KSIF), MDD algorithm (abbreviated as MDD), the method of the present invention (abbreviated as LK). In order to better evaluate the performance of various importance evaluation methods, the discrimination index M is introduced here. The discrimination index is defined as follows:
其中,R为网络节点重要性的等级向量,n为向量R的总等级数,nr为第r等级中的节点数量。如果所有节点在同一重要性等级中,区分度指标M的值为0,相应评估方法无法区分每个节点的重要性。如果每一个重要性等级中只包含1个节点,区分度指标M的值为1,相应评估方法能够有效地区分每个节点的重要性,具有最强的区分能力。Among them, R is the level vector of the importance of network nodes, n is the total number of levels of the vector R, and n r is the number of nodes in the rth level. If all nodes are in the same importance level, the value of the discrimination index M is 0, and the corresponding evaluation method cannot distinguish the importance of each node. If there is only one node in each importance level, and the value of the discrimination index M is 1, the corresponding evaluation method can effectively distinguish the importance of each node and has the strongest discrimination ability.
首先,选取实施例三中(图3)所示的示例网络,采用上述7种方法对示例网络节点重要性进行评估,并按照重要度对节点进行排序,排序结果如表4所示(表4的每一列对应一种重要性评估方法,同一等级的节点具有相同的重要度,“其它”表示剩余的所有节点)。从表4可以看出,与现有的6种典型方法相比,本发明公开的方法能够准确、细致地区分网络节点的重要性,示例性地,每个重要性等级的节点数量最多为2个。First, select the example network shown in Example 3 (Figure 3), use the above seven methods to evaluate the importance of the example network nodes, and sort the nodes according to the importance, the sorting results are shown in Table 4 (Table 4 Each column of corresponds to an importance evaluation method, nodes of the same level have the same importance, and "other" means all remaining nodes). As can be seen from Table 4, compared with the existing 6 typical methods, the method disclosed in the present invention can accurately and finely distinguish the importance of network nodes. Exemplarily, the number of nodes in each importance level is at most 2 indivual.
表4:示例网络节点重要性的排序结果Table 4: Ranking results of node importance in an example network
为了进一步说明本发明方法的有益效果,选取11个不同规模的真实网络(包括:Karate Club网络、Dolphin网络、Jazz网络、Prison网络、NetScience网络、Book网络、Celegan网络、E-mail网络、Blogs网络、PGP网络和Enron网络),分析比较上述7种重要性评估方法的区分度指标M。表5显示了7种重要性评估方法对11个真实网络节点重要性的区分能力。可以看出:针对选取的11个真实网络,本发明公开的方法都能够获得最大的区分度值。相对于其它6种节点重要性评估方法,本发明的方法更能够细致、准确地识别真实网络节点的重要性。In order to further illustrate the beneficial effect of the inventive method, choose 11 real networks of different scales (comprising: Karate Club network, Dolphin network, Jazz network, Prison network, NetScience network, Book network, Celegan network, E-mail network, Blogs network , PGP network and Enron network), analyze and compare the discrimination index M of the above seven importance evaluation methods. Table 5 shows the discriminative ability of 7 importance evaluation methods on the importance of 11 real network nodes. It can be seen that for the selected 11 real networks, the methods disclosed in the present invention can all obtain the maximum discrimination value. Compared with the other six node importance evaluation methods, the method of the present invention can more carefully and accurately identify the importance of real network nodes.
表5:不同重要性评估方法对真实网络节点重要性的区分能力Table 5: Discrimination ability of different importance evaluation methods on the importance of real network nodes
为了更加直观的展示本发明方法的有益效果,采用互补累积分布函数(CCDF)对表5中数据进行展示。图4~图7分别显示了4个网络(实施例三中网络、Karate Club网络、Dolphin网络和Celegan网络)的CCDF。按照CCDF的原理,如果位于同一重要性等级的节点数量越多,CCDF下降越快,反之,CCDF则会沿斜对角线缓慢下降。从图4~图7可以看出,本发明方法的CCDF沿斜对角线缓慢下降,说明本发明所述方法能够将社交网络中节点间的重要性差异很好地区分开来。In order to demonstrate the beneficial effect of the method of the present invention more intuitively, the data in Table 5 is displayed by using a complementary cumulative distribution function (CCDF). Figures 4 to 7 respectively show the CCDFs of the four networks (the network in Embodiment 3, the Karate Club network, the Dolphin network and the Celegan network). According to the principle of CCDF, if the number of nodes at the same importance level is more, the CCDF will decrease faster, otherwise, CCDF will decrease slowly along the diagonal. It can be seen from Fig. 4 to Fig. 7 that the CCDF of the method of the present invention decreases slowly along the diagonal line, indicating that the method of the present invention can well distinguish the importance differences between nodes in the social network.
另外,借助LFR网络生成器生成人工社交网络,利用人工社交网络对本发明方法进行评估。LFR网络生成器有4个重要参数,分别是节点规模n(number of nodes),平均节点度k(average degree of nodes),社区结构混合参数μ(mixing parameter of communitystructure)以及度幂律分布λ(power-law of degree distribution)。上述4个参数的变化将影响人工社交网络的拓扑结构。图8~图11分别显示了4个参数在保持1个参数变化,其余3个参数不变时,不同节点重要性评估方法区分度指标M的变化情况。可以看出:针对该人工社交网络,本发明所述的方法能够获得最大的区分度值。说明较之其它5种节点重要性评估方法(H、K、KS、KS-IF、MDD),本发明所述方法更能够细致、准确地识别人工社交网络的节点重要性。In addition, the artificial social network is generated by means of the LFR network generator, and the method of the present invention is evaluated by using the artificial social network. The LFR network generator has four important parameters, which are node size n (number of nodes), average node degree k (average degree of nodes), community structure mixing parameter μ (mixing parameter of community structure) and degree power law distribution λ ( power-law of degree distribution). The changes of the above four parameters will affect the topology of the artificial social network. Figures 8 to 11 respectively show the changes of the discrimination index M of different node importance evaluation methods when one of the four parameters is kept changing and the other three parameters remain unchanged. It can be seen that: for the artificial social network, the method of the present invention can obtain the maximum discrimination value. It shows that compared with other five node importance evaluation methods (H, K, KS, KS-IF, MDD), the method of the present invention can identify the node importance of artificial social network more carefully and accurately.
本领域技术人员可以理解,实现上述实施例方法的全部或部分流程,可以通过计算机程序指令相关的硬件来完成,所述的程序可存储于计算机可读存储介质中。其中,所述计算机可读存储介质为磁盘、光盘、只读存储记忆体或随机存储记忆体等。Those skilled in the art can understand that all or part of the processes of the methods in the above embodiments can be implemented by computer program instructions related hardware, and the program can be stored in a computer-readable storage medium. Wherein, the computer-readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, and the like.
以上所述,仅为本发明较佳的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到的变化或替换,都应涵盖在本发明的保护范围之内。The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present invention can easily think of changes or Replacement should be covered within the protection scope of the present invention.
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810744899.6A CN109034562B (en) | 2018-07-09 | 2018-07-09 | A method and system for evaluating the importance of social network nodes |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810744899.6A CN109034562B (en) | 2018-07-09 | 2018-07-09 | A method and system for evaluating the importance of social network nodes |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109034562A true CN109034562A (en) | 2018-12-18 |
CN109034562B CN109034562B (en) | 2021-07-23 |
Family
ID=64640764
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810744899.6A Expired - Fee Related CN109034562B (en) | 2018-07-09 | 2018-07-09 | A method and system for evaluating the importance of social network nodes |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109034562B (en) |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110826164A (en) * | 2019-11-06 | 2020-02-21 | 中国人民解放军国防科技大学 | Complex network node importance evaluation method based on local and global connectivity |
CN111008311A (en) * | 2019-12-25 | 2020-04-14 | 中国人民解放军国防科技大学 | Complex network node importance evaluation method and device based on neighborhood weak connection |
CN111127233A (en) * | 2019-12-26 | 2020-05-08 | 华中科技大学 | A method for calculating user kernel value in undirected weighted graph of social network |
CN111178678A (en) * | 2019-12-06 | 2020-05-19 | 中国人民解放军战略支援部队信息工程大学 | Network Node Importance Evaluation Method Based on Community Influence |
WO2020125721A1 (en) * | 2018-12-21 | 2020-06-25 | 腾讯科技(深圳)有限公司 | Method and device for determining social hierarchies of nodes in social networking |
CN111368147A (en) * | 2020-02-25 | 2020-07-03 | 支付宝(杭州)信息技术有限公司 | Graph feature processing method and device |
CN111612641A (en) * | 2020-04-30 | 2020-09-01 | 兰州理工大学 | A method for identifying influential users in social networks |
CN113723504A (en) * | 2021-08-28 | 2021-11-30 | 重庆理工大学 | Method for identifying influential propagators in complex network |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105335892A (en) * | 2015-10-30 | 2016-02-17 | 南京邮电大学 | Realization method for discovering important users of social network |
CN105471637A (en) * | 2015-11-20 | 2016-04-06 | 中国矿业大学 | Evaluation method and system for importance of node of complex network |
US20170353825A1 (en) * | 2016-06-07 | 2017-12-07 | NinthDecimal, Inc. | Systems and Methods to Track Locations Visited by Mobile Devices and Determine Neighbors of and Distances among Locations |
-
2018
- 2018-07-09 CN CN201810744899.6A patent/CN109034562B/en not_active Expired - Fee Related
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105335892A (en) * | 2015-10-30 | 2016-02-17 | 南京邮电大学 | Realization method for discovering important users of social network |
CN105471637A (en) * | 2015-11-20 | 2016-04-06 | 中国矿业大学 | Evaluation method and system for importance of node of complex network |
US20170353825A1 (en) * | 2016-06-07 | 2017-12-07 | NinthDecimal, Inc. | Systems and Methods to Track Locations Visited by Mobile Devices and Determine Neighbors of and Distances among Locations |
Non-Patent Citations (1)
Title |
---|
李泽鹏等: "基于社交网络结构的节点影响力度量方法", 《电子学报》 * |
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2020125721A1 (en) * | 2018-12-21 | 2020-06-25 | 腾讯科技(深圳)有限公司 | Method and device for determining social hierarchies of nodes in social networking |
US12229838B2 (en) | 2018-12-21 | 2025-02-18 | Tencent Technology (Shenzhen) Company Limited | Method and device for determining social rank of node in social network |
CN110826164B (en) * | 2019-11-06 | 2023-07-04 | 中国人民解放军国防科技大学 | A Method for Evaluating the Importance of Complex Network Nodes Based on Local and Global Connectivity |
CN110826164A (en) * | 2019-11-06 | 2020-02-21 | 中国人民解放军国防科技大学 | Complex network node importance evaluation method based on local and global connectivity |
CN111178678B (en) * | 2019-12-06 | 2022-11-08 | 中国人民解放军战略支援部队信息工程大学 | Network Node Importance Evaluation Method Based on Community Influence |
CN111178678A (en) * | 2019-12-06 | 2020-05-19 | 中国人民解放军战略支援部队信息工程大学 | Network Node Importance Evaluation Method Based on Community Influence |
CN111008311B (en) * | 2019-12-25 | 2023-07-21 | 中国人民解放军国防科技大学 | Method and device for evaluating the importance of complex network nodes based on neighborhood weak connections |
CN111008311A (en) * | 2019-12-25 | 2020-04-14 | 中国人民解放军国防科技大学 | Complex network node importance evaluation method and device based on neighborhood weak connection |
CN111127233A (en) * | 2019-12-26 | 2020-05-08 | 华中科技大学 | A method for calculating user kernel value in undirected weighted graph of social network |
CN111368147A (en) * | 2020-02-25 | 2020-07-03 | 支付宝(杭州)信息技术有限公司 | Graph feature processing method and device |
CN111368147B (en) * | 2020-02-25 | 2021-07-06 | 支付宝(杭州)信息技术有限公司 | Graph feature processing method and device |
CN111612641A (en) * | 2020-04-30 | 2020-09-01 | 兰州理工大学 | A method for identifying influential users in social networks |
CN113723504A (en) * | 2021-08-28 | 2021-11-30 | 重庆理工大学 | Method for identifying influential propagators in complex network |
CN113723504B (en) * | 2021-08-28 | 2023-05-16 | 重庆理工大学 | Method for identifying influential propagators in complex network |
Also Published As
Publication number | Publication date |
---|---|
CN109034562B (en) | 2021-07-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109034562B (en) | A method and system for evaluating the importance of social network nodes | |
Liu et al. | A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks | |
Ghosh et al. | The interplay between dynamics and networks: centrality, communities, and cheeger inequality | |
Xu et al. | TNS-LPA: an improved label propagation algorithm for community detection based on two-level neighbourhood similarity | |
CN107895038A (en) | A kind of link prediction relation recommends method and device | |
CN111882198A (en) | A project performance evaluation method and system | |
CN103559303A (en) | Evaluation and selection method for data mining algorithm | |
CN110705045A (en) | Link prediction method for constructing weighting network by using network topological characteristics | |
JP7157328B2 (en) | Graph simplification method, graph simplification program, and information processing device | |
Zhao et al. | Identifying influential spreaders in social networks via normalized local structure attributes | |
CN113886373A (en) | Data processing method and device and electronic equipment | |
CN115563477B (en) | Harmonic data identification method, device, computer equipment and storage medium | |
Liang et al. | Identifying influential spreaders in complex networks based on local and global structure | |
Amin et al. | Advanced community identification model for social networks | |
Du Plessis et al. | A cosine rule-based discrete sectional curvature for graphs | |
CN107957944B (en) | User data coverage rate oriented test case automatic generation method | |
CN105740907A (en) | Local community mining method | |
Li et al. | A community merger of optimization algorithm to extract overlapping communities in networks | |
CN109436980A (en) | The condition detection method and system of elevator components | |
Pan et al. | A spin-glass model based local community detection method in social networks | |
CN113837368A (en) | Control method and device for evaluating data value of each participant in federal learning | |
CN114443336A (en) | Abnormal root cause positioning method and device, electronic equipment and storage medium | |
Nurkasyifah et al. | Supervised GNNs for Node Label Classification in Highly Sparse Network: Comparative Analysis | |
Zhao et al. | Local community detection via edge weighting | |
CN118397004B (en) | A method and device for calculating criticality index of vector geographic data |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210723 |