CN108428044A - A kind of employee's importance ranking method based on network - Google Patents

A kind of employee's importance ranking method based on network Download PDF

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CN108428044A
CN108428044A CN201810107977.1A CN201810107977A CN108428044A CN 108428044 A CN108428044 A CN 108428044A CN 201810107977 A CN201810107977 A CN 201810107977A CN 108428044 A CN108428044 A CN 108428044A
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宣琦
虞烨炜
李永苗
郑钧
俞山青
徐东伟
阮中远
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Zhejiang University of Technology ZJUT
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Abstract

A kind of employee's importance ranking method based on network, includes the following steps：S1 establishes the weighting Undirected networks between two task places by the record data in employee's task place；S2 will be that undirected Task Network is weighted by node of task type by the task place network transformation that node, task type are even side of task place；Node topology attribute in S3 calculating task networks, is ranked up the importance of task node；Employee is mapped in Task Network by S4, obtains the importance ranking of employee.It is to weight undirected Task Network that the present invention, which will weight undirected task place network transformation, the common counter of calculating task node importance in network sequence, and obtains the importance ranking to task.Employee is mapped to Task Network, in conjunction with task significance sequence and task type and number of tasks, obtains the importance ranking of employee.

Description

A kind of employee's importance ranking method based on network
Technical field
The present invention relates to data mining and nodes analysis technical field, more particularly to a kind of based on network Employee's importance ranking method.
Background technology
It finds the key node in network or evaluates significance level of some node relative to other nodes, be network section Important research content and one of major issue urgently to be resolved hurrily.With the fast development of information technology, filled in people's life Expire various networks, such as communication network, electric power networks, transportation network and social networks etc., and these networks are complicated and huge, Become most important with clearly quantitative index to measure the significance level of nodes.In one network, different nodes Play effect of different sizes.Such as in transportation network, there is very important transport hub, also there is irrelevant spare transfer It stands；Such as in social networks, the large size for the leader that has complaints also has an impact small bean vermicelli.Important node to the structure of network and Function has tremendous influence, the sequence of node importance and the excavation of important node significant.
Task significance sequence is to discriminate between significance level of the task in entire work system, rational task significance row Sequence can help to the reasonable distribution of effect and the follow-up work of assessment task.Task significance also embodies the contribution journey of employee Degree, affects the job performance of employee.The task type being responsible for from the importance ranking of task and employee considers, can reflect Go out the importance of employee in systems.It can be conducive to investigate the work achievement of employee in time to the assessment of employee's importance, it is right Employee implements effective rewards and punishments and targetedly cultivates.
Patent 201510406579.6 proposes a kind of method of multitiered network node importance sequence, and essential core is meter It calculates the nodal community per layer network and obtains each layer influence power weight using analytic hierarchy process (AHP), but be not particularly suited for single layer network Employee's importance ranking.Patent 201510373034.X discloses a kind of complex network important node sequence side based on group's degree Method and model evolution method obtain network Zhong Ge nodes group's degree and calculate each rank entirety of network group degree, but important to node The evaluation index of property only has group's degree, and the scope of application is than narrow.Patent 201310413387.9 relates to communication network node Assessment of Important weights the various nodal communities of weighted network to obtain comprehensive evaluation index, but the foundation of network is not related to The transformation of figure, therefore it is not suitable for the Task Network sequence of employee.
Invention content
In order to overcome the shortcomings of that traditional employee's ranking is single unilateral, for Hospital Logistic delivery system, the present invention will It is using task type as the Task Network of node by the task place network transformation that node, task type are even side of task place Network sorts to the task significance in network, and employee is mapped to Task Network, in conjunction with task significance sequence and task class Type and number of tasks obtain the importance ranking of employee.
The technical solution adopted by the present invention to solve the technical problems is as follows：
A kind of employee's importance ranking method based on network, includes the following steps：
S1：By the record data in employee's task place, the weighting Undirected networks between two task places are established；
S2：To be to be with task type by the task place network transformation that node, task type are even side of task place Node weights undirected Task Network；
S3：Node topology attribute in calculating task network, is ranked up the importance of task node；
S4：Employee is mapped in Task Network, obtains the importance ranking of employee.
Further, in the step S1, two tasks involved by this task are obtained from the task record of employee Place, with establishing task spot net；Same task type is reflected when sequence is opposite due to identical when two task places, institute Network with foundation is undirected；Number of tasks between two task places reflects the frequent degree of task execution, as Company's side right weight of network；The weighted undirected graph of foundation is expressed as G=(V, E, W), wherein V=[υ1,υ2,…,υM] indicate each Task place, E=[e1,e2,…,eN] indicate that there are task nexus, W=[w between each place1,w2,…,wN] indicate each The number of place task；Undirected networks are weighted according to obtained task place, calculate the intensity of each node, then the intensity of node i It is defined as
Wherein weighted network G includes M node, and weight matrix is W=(wij)。
Further, in the step S2, according to task spot net, transformation obtains the weighting Undirected networks of task；Appoint There is between two places task then to there is even side between two point nodes in business ground spot net, even side is practical illustrates certain Service type；It needs to weigh the importance of task, but due to relatively difficult to the sequence of even side importance in network, therefore needs The company side for the task that indicates is transformed to the node of task, and exists if having common task place between task node and connects Side constitutes and weights undirected Task Network L (G)；Since predecessor is engaged in connecting number of edges in ground spot net being N, the then Task Network converted The node number of network is N.The weight definition of network is after transformation
Wherein wijIndicate company's side right weight between predecessor's business place network G interior joint i and node j, wikIndicate predecessor's business ground Company's side right weight between spot net G interior joints i and node k, siIndicate that the intensity of node i, i-j and i-k are indicated respectively by scheming in G Task node of the company side that node i, j and node i, k are constituted in figure L (G), wL(i-j,i-k)It indicates to appoint in network L (G) after converting Weight between business node i-j and i-k.
Further, in the step S3, using the established undirected Task Network of weighting, network node importance is calculated Multiple indexs of sequence, for the measurement to task significance；
The value of a node is often depending on the position of this node in a network in network, and degree centrality is to weigh section The position of point in a network, is defined as
Node is weighed by average value of the calculate node at a distance from all nodes of other in network close to centrality Importance is defined as
Wherein diIndicate the average value of the distance of all nodes in node i to network, dijIt is distance of the node i to node j. diThe relative size of value reflects the relative importance of node i in a network to a certain extent；
Betweenness center is in network in the shortest path of all nodes pair, and the shortest path number by a node is more Node is more important, is defined as
Wherein gstFor the number from node s to the shortest path of node t, ns i tFor from node s to the g of node tstItem is most short The number of shortest path in path Jing Guo node i；
For weighted network, the shortest path between two nodes be defined as connection two nodes side weights it With minimum path, used here as classical dijkstra's algorithm；
Eigenvector centrality thinks that the importance of a node had both depended on the quantity of its neighbor node, also depends on every The importance of a neighbor node remembers xiFor the importance measures value of the i of node, then
Wherein c is proportionality constant.Remember x=[x1,x2,x3,…,xn]T, it is written as when reaching stable state by successive ignition Matrix form
X=cAx
This indicates that x is the characteristic value c of matrix A-1Corresponding feature vector, the basic skills for calculating vector x are given initial value x (0), iterative algorithm is then used
X (t)=cAx (t-1), t=1,2 ...,
Until normalized x'(t)=x'(t-1) until, equation can obtain a convergent untrivialo solution, i.e. x=λ-1Ax；
The web-based link structure of PageRank algorithms to webpage sorting, it considers that in WWW page it is important Property depend on be directed toward its other pages quality and quantity, i.e.,
Multiple node importances sequence index comprehensive of calculating is considered by each node, is calculated flat after its normalization Importance measures score of the mean value as each task nodeT。
In the step S4, according to the importance ranking of task, task type in conjunction with involved by each employee and corresponding Number of tasks obtains the importance measures of each employee, is defined as
Wherein ∑ CpIndicate the general assignment number of employee p, Cp,qIndicate number of tasks of the employee p on task q, scoreT,qIt indicates The importance measures of task q, scoreE,pIndicate the importance measures of employee p；
To the score score of each employeeEIt is ranked up, obtains the importance ranking of employee.
The applicable object of the present invention, which is that similar Hospital Logistic transport is this kind of, has whole user behavioral data, but does not have user The company of the private datas such as identity.The present invention transports data instance to study Zhejiang Hospital's logistics, with will weighting undirected task Spot net is transformed to weight undirected Task Network, the common counter of calculating task node importance in network sequence, and obtains To the importance ranking of task.Employee is mapped to Task Network, in conjunction with task significance sequence and task type and number of tasks, Obtain the importance ranking of employee.
Beneficial effects of the present invention are：With Network Science to the sort method of node importance, from network structure Employee's importance is assessed, topological structure is taken into account and non-Analysis of Topological Structure works well.
Description of the drawings
Fig. 1 is the flow chart of employee's importance ranking method based on network of the embodiment of the present invention；
Fig. 2 is the task ground spot net of the embodiment of the present invention；
Fig. 3 is the Task Network of the embodiment of the present invention；
Fig. 4 is employee's importance ranking of the embodiment of the present invention.
Specific implementation mode
The present invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig.1~Fig. 4, a kind of employee's importance ranking method based on network, the present invention is to study Zhejiang Hospital Data instance is transported in logistics, and will weight undirected task place network transformation is the undirected Task Network of weighting, calculating task net The common counter of network interior joint importance ranking, and obtain the importance ranking to task；Employee is mapped to Task Network, is tied Task significance sequence and task type and number of tasks are closed, the importance ranking of employee is obtained.
The present invention is divided into following steps：
S1：By the record data in employee's task place, the weighting Undirected networks between two task places are established；
S2：To be to be with task type by the task place network transformation that node, task type are even side of task place Node weights undirected Task Network；
S3：Node topology attribute in calculating task network, is ranked up the importance of task node；
S4：Employee is mapped in Task Network, obtains the importance ranking of employee.
Further, in the step S1, two tasks involved by this task are obtained from the task record of employee Place, with establishing task spot net；Same task type is reflected when sequence is opposite due to identical when two task places, institute Network with foundation is undirected；Number of tasks between two task places reflects the frequent degree of task execution, as Company's side right weight of network.The weighted undirected graph of foundation is expressed as G=(V, E, W), wherein V=[υ1,υ2,…,υM] indicate each Task place, E=[e1,e2,…,eN] indicate that there are task nexus, W=[w between each place1,w2,…,wN] indicate each The number of place task；Undirected networks are weighted according to obtained task place, calculate the intensity of each node, then the intensity of node i It is defined as
Wherein weighted network G includes M node, and weight matrix is W=(wij)。
Further, in the step S2, according to task spot net, transformation obtains the weighting Undirected networks of task, appoints There is between two places task then to there is even side between two point nodes in business ground spot net, even side is practical illustrates certain Service type.It needs to weigh the importance of task, but due to relatively difficult to the sequence of even side importance in network, therefore needs The company side for the task that indicates is transformed to the node of task, and exists if having common task place between task node and connects Side constitutes and weights undirected Task Network L (G), to the task node in Task Network carry out analysis can be obtained by about The importance ranking of task, since predecessor is engaged in connecting number of edges in ground spot net being N, then the node number of the Task Network after converting is N, the weight definition of network is the average value of the probability of random walk between connecting side in predecessor's business ground spot net after transformation, is indicated For
Wherein wijIndicate company's side right weight between predecessor's business place network G interior joint i and node j, wikIndicate predecessor's business ground Company's side right weight between spot net G interior joints i and node k, siIndicate that the intensity of node i, i-j and i-k are indicated respectively by scheming in G Task node of the company side that node i, j and node i, k are constituted in figure L (G), wL(i-j,i-k)It indicates to appoint in network L (G) after converting Weight between business node i-j and i-k.
Further, in the step S3, using the established undirected Task Network of weighting, network node importance is calculated Multiple indexs of sequence, for the measurement to task significance；
The value of a node is often depending on the position of this node in a network in network, and degree centrality is to weigh section The position of point in a network, is defined as
Node is weighed by average value of the calculate node at a distance from all nodes of other in network close to centrality Importance is defined as
Wherein diIndicate the average value of the distance of all nodes in node i to network, dijIt is distance of the node i to node j. diThe relative size of value reflects the relative importance of node i in a network to a certain extent；
Betweenness center is in network in the shortest path of all nodes pair, and the shortest path number by a node is more Node is more important, is defined as
Wherein gstFor the number from node s to the shortest path of node t,For from node s to the g of node tstItem is most short The number of shortest path in path Jing Guo node i；
For weighted network, the shortest path between two nodes be defined as connection two nodes side weights it With minimum path, used here as classical dijkstra's algorithm；
Eigenvector centrality thinks that the importance of a node had both depended on the quantity of its neighbor node, also depends on every The importance of a neighbor node.Remember xiFor the importance measures value of the i of node, then
Wherein c is proportionality constant, note x=[x1,x2,x3,…,xn]T, it is written as when reaching stable state by successive ignition Matrix form
X=cAx
This indicates that x is the characteristic value c of matrix A-1Corresponding feature vector, the basic skills for calculating vector x are given initial value x (0), iterative algorithm is then used
X (t)=cAx (t-1), t=1,2 ...,
Until normalized x'(t)=x'(t-1) until, equation can obtain a convergent untrivialo solution, i.e. x=λ-1Ax；
The web-based link structure of PageRank algorithms to webpage sorting, it considers that in WWW page it is important Property depend on be directed toward its other pages quality and quantity, i.e.,
Each node is considered multiple node importances sequence index comprehensive of calculating, since each index size does not exist Same scale, directly use can make the decreased effectiveness of part index number, therefore first normalize it respectively, then calculate its average value conduct The importance measures score of each task nodeT。
In the step S4, according to the importance ranking of task, task type in conjunction with involved by each employee and corresponding Number of tasks obtains the importance measures of each employee, is defined as
Wherein ∑ CpIndicate the general assignment number of employee p, Cp,qIndicate number of tasks of the employee p on task q, scoreT,qIt indicates The importance measures of task q, scoreE,pIndicate the importance measures of employee p；
To the score score of each employeeEIt is ranked up, obtains the importance ranking of employee.
It is as described above the embodiment introduction of present invention employee's sort method in Hospital Logistic task, it will be with task place It is undirected using task type as the weighting of weight for the weighting undirected task place network transformation that node, task type are weight Task Network, the common counter of calculating task node importance in network sequence, and obtain the importance ranking to task.By member Work is mapped to Task Network, in conjunction with task significance sequence and task type and number of tasks, obtains the importance ranking of employee.From Various dimensions weigh the ranking of Hospital Logistic employee, have reached the requirement of actual use.It is merely illustrative for invention, and It is unrestricted.Those skilled in the art understand that can be permitted it in the spirit and scope defined by invention claim It is change more, it changes or even equivalent, but fall in protection scope of the present invention.

Claims (5)

1. a kind of employee's importance ranking method based on network, it is characterised in that：The sort method includes the following steps：
S1：By the record data in employee's task place, the weighting Undirected networks between two task places are established；
S2：To be using task type as node by the task place network transformation that node, task type are even side of task place Weight undirected Task Network；
S3：Node topology attribute in calculating task network, is ranked up the importance of task node；
S4：Employee is mapped in Task Network, obtains the importance ranking of employee.
2. a kind of employee's importance ranking method based on network as described in claim 1, it is characterised in that：The step In S1, two task places involved by this task are obtained from the task record of employee, with establishing task spot net；By Same task type is reflected when and sequence identical when two task places is opposite, so the network established is undirected； Number of tasks between two task places reflects the frequent degree of task execution, company's side right weight as network；It establishes Weighted undirected graph is expressed as G=(V, E, W), wherein V=[υ1,υ2,…,υM] indicate each task place, E=[e1,e2,…, eN] indicate that there are task nexus, W=[w between each place1,w2,…,wN] indicate the number of each place task；According to The task place weighting Undirected networks arrived, calculate the intensity of each node, then the strength definition of node i is
Wherein weighted network G includes M node, and weight matrix is W=(wij)。
3. a kind of employee's importance ranking method based on network as claimed in claim 1 or 2, it is characterised in that：It is described In step S2, according to task spot net, transformation obtains the weighting Undirected networks of task；Task in spot net two places it Between there is task then to there is even side between two point nodes, even side is practical illustrates certain task type；Need the weight to task The property wanted is weighed, but due to relatively difficult to the sequence of even side importance in network, therefore is needed the Lian Bianbian for the task that indicates It is changed to the node of task, and there is even side if having common task place between task node, constitutes undirected times of weighting Be engaged in network L (G)；Since predecessor is engaged in connecting number of edges in ground spot net being N, then the node number of the Task Network converted is N.After transformation The weight definition of network is
Wherein wijIndicate company's side right weight between predecessor's business place network G interior joint i and node j, wikIndicate predecessor's business place net Company's side right weight between network G interior joints i and node k, siIndicate the intensity of node i；I-j and i-k is indicated respectively by scheming G interior joints I, task node of the company side that j and node i, k are constituted in figure L (G), wL(i-j,i-k)Task section in network L (G) after expression transformation Weight between point i-j and i-k.
4. a kind of employee's importance ranking method based on network as claimed in claim 1 or 2, it is characterised in that：It is described In step S3, using the undirected Task Network of established weighting, calculate multiple indexs of network node importance ranking, for pair The measurement of task significance；
The value of a node is often depending on the position of this node in a network in network, and degree centrality is to weigh node to exist Position in network, is defined as
The important of node is weighed by average value of the calculate node at a distance from all nodes of other in network close to centrality Property, it is defined as
Wherein diIndicate the average value of the distance of all nodes in node i to network, dijIt is distance of the node i to node j.diValue Relative size reflect the relative importance of node i in a network to a certain extent；
Betweenness center is to get over multinode by the shortest path number of a node in network in the shortest path of all nodes pair It is more important, it is defined as
Wherein gstFor the number from node s to the shortest path of node t,For from node s to the g of node tstIn shortest path The number of shortest path by node i；
For weighted network, the shortest path between two nodes is defined as the weights sum on the side of two nodes of connection most Small path, used here as classical dijkstra's algorithm；
Eigenvector centrality thinks that the importance of a node had both depended on the quantity of its neighbor node, also depends on each neighbour The importance of node is occupied, remembers xiFor the importance measures value of the i of node, then
Wherein c is proportionality constant, note x=[x1,x2,x3,…,xn]T, by the matrix being written as when successive ignition arrival stable state Form
X=cAx
This indicates that x is the characteristic value c of matrix A-1Corresponding feature vector, the basic skills for calculating vector x are given initial value x (0), Then iterative algorithm is used
X (t)=cAx (t-1), t=1,2 ...,
Until normalized x'(t)=x'(t-1) until, equation can obtain a convergent untrivialo solution, i.e. x=λ-1Ax；
The web-based link structure of PageRank algorithms is to webpage sorting, it considers that the importance of a page takes in WWW Certainly in the quality and quantity for being directed toward its other pages, i.e.,
Multiple node importances sequence index comprehensive of calculating is considered by each node, calculates the average value after its normalization Importance measures score as each task nodeT。
5. a kind of employee's importance ranking method based on network as claimed in claim 1 or 2, it is characterised in that：It is described In step S4, according to the importance ranking of task, the task type in conjunction with involved by each employee and corresponding task number obtain every The importance measures of a employee, are defined as
Wherein ∑ CpIndicate the general assignment number of employee p, Cp,qIndicate number of tasks of the employee p on task q, scoreT,qExpression task q Importance measures, scoreE,pIndicate the importance measures of employee p；
To the score score of each employeeEIt is ranked up, obtains the importance ranking of employee.
CN201810107977.1A 2018-02-02 2018-02-02 A kind of employee's importance ranking method based on network Pending CN108428044A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109272225A (en) * 2018-09-07 2019-01-25 大连海事大学 A kind of Bug tracing system tester importance ranking method
CN109376984A (en) * 2018-09-03 2019-02-22 杭州医好网络科技有限公司 A kind of employee's sort method based on Hospital Logistic classification transport task
CN109446628A (en) * 2018-10-22 2019-03-08 太原科技大学 The building of multilayer city traffic network and key node recognition methods based on complex network

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109376984A (en) * 2018-09-03 2019-02-22 杭州医好网络科技有限公司 A kind of employee's sort method based on Hospital Logistic classification transport task
CN109272225A (en) * 2018-09-07 2019-01-25 大连海事大学 A kind of Bug tracing system tester importance ranking method
CN109446628A (en) * 2018-10-22 2019-03-08 太原科技大学 The building of multilayer city traffic network and key node recognition methods based on complex network
CN109446628B (en) * 2018-10-22 2022-04-19 太原科技大学 Multi-layer urban traffic network construction and key node identification method based on complex network

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