CN108399491B - Employee diversity ordering method based on network graph - Google Patents

Employee diversity ordering method based on network graph Download PDF

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CN108399491B
CN108399491B CN201810106887.0A CN201810106887A CN108399491B CN 108399491 B CN108399491 B CN 108399491B CN 201810106887 A CN201810106887 A CN 201810106887A CN 108399491 B CN108399491 B CN 108399491B
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宣琦
虞烨炜
郑钧
李永苗
阮中远
徐东伟
俞山青
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Zhejiang University of Technology ZJUT
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Abstract

A staff diversity ordering method based on a network diagram comprises the following steps: s1, establishing a weighted undirected network among task sites through the data of the task sites of the staff; s2, converting the task place network into a weighted undirected task network taking the task type as a node; s3, obtaining a preliminary ranking of the diversity of the employees according to the coverage degree of the task type of each employee in the task network; s4, calculating the difference of task types of employees with similar network coverage degrees; s5, integrating the network coverage degree and task diversity of the employee task type to obtain the final ranking of the employee diversity. The invention transforms the weighted undirected task place network into the task network, and combines the difference of the employee task sets to obtain the sequence of the diversity of the employees according to the coverage degree of the employee task types in the task network.

Description

Employee diversity ordering method based on network graph
Technical Field
The invention relates to the field of node diversity in data mining and network science, in particular to a staff diversity ordering method based on a network diagram.
Background
There are various things, from macro to micro, there are various ecological environments and social cultures, there are various biological species and commercial products, and there are also various genetic genes of the same species. The diversity of each thing will usually keep dynamic balance, more adaptive to the environment. The diversity of things is the basis and key to the proper and sustained operation of the system. Diversification becomes a necessary trend of social development, the diversity of employees is the employees of enterprises which need various skills in the composition of human resources, and the diversified employee management is that the individual characteristics of the employees of the enterprises keep certain difference on the premise of obeying a common organization concept. The diversification of the staff enables the organization to become an integral body with intelligent complementation, reasonable energy level, consistent length, consistent target and coordinated cooperation, so that the system has higher robustness and stability.
The sequencing of the diversity of the staff is necessary, the diversity of the staff can be comprehensively measured, the working condition of the staff can be inspected in time or the change of the staff can be reasonably arranged, and the working direction of the staff is guided. Too single staff structure can cause the rigidity of enterprise's operation, makes the operation of enterprise lack flexibility and mobility, is unfavorable for the stability and the sustainable development of system. The staff diversity ordering can reasonably evaluate the diversity of each staff, and reasonably plan the distribution of the task types, thereby being beneficial to promoting the joint development of staff individuals and enterprises. After the diversity ordering, the diversity difference among the employees can be obtained, and the evaluation and guidance significance is provided for the subsequent work of the employees.
Patent 201410531309.3 discloses a binary tree node sorting-based a star path finding method and system, which focuses on optimizing the efficiency when using an a star algorithm to sort nodes, but does not relate to diversity sorting among nodes. Patent 201310413387.9 relates to evaluation of importance of nodes in a communication network, and weights various node attributes in a weighting network to obtain a comprehensive evaluation index, but mainly, evaluation of importance of nodes does not include analysis of diversity, and therefore, is not suitable for diversity ranking of employees. Patent 201610218405.1 relates to a keyword search method based on diversity and scale characteristics, which returns keyword-based tuple information for the link relationship between the input keywords and tuples, but is not suitable for the ordering of nodes in the network. In view of the defects, the invention transforms the weighted undirected task place network into the task network, and combines the difference of the employee task sets to obtain the sequence of the diversity of the employees according to the coverage degree of the employee task types in the task network.
Disclosure of Invention
In order to overcome the defect of single traditional employee ranking method, the invention provides a network diagram-based employee diversity ranking method, wherein a weighted undirected task place network is converted into a task network, and the diversity ranking of employees is obtained by combining the difference of employee task sets according to the coverage degree of employee task types in the task network.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a staff diversity ordering method based on a network diagram comprises the following steps:
s1: establishing a weighted undirected network among task sites through the data of the task sites of the staff;
s2: converting the task site network into a weighted undirected task network with task types as nodes;
s3: obtaining a preliminary ranking of the diversity of the employees according to the coverage degree of the task type of each employee in the task network;
s4: calculating the difference of task types of the employees with similar network coverage degrees;
s5: and integrating the network coverage degree and the task difference of the task type of the staff to obtain the diversity final ranking of the staff.
Further, in step S1, a task location network is established according to the relation between task locations in the employee task data; the number of the connections between the task sites is different, the number of the tasks between the two sites is used as a continuous edge weight in the task site network, and the established weighted undirected graph is represented as G (V, E, W), wherein V [ upsilon [ ]1,υ2,…,υM]Representing each task location, E ═ E1,e2,…,eN]Indicating that there is a task relationship between task locations, W ═ W1,w2,…,wN]Indicating the number of tasks of each place; according to the obtained task location weighted undirected network, calculating the intensity of each node, and defining the intensity of the node i as
Figure BDA0001568003030000021
Wherein the weighting network G comprises M nodes and a weight matrixIs W ═ Wij)。
Further, in step S2, mapping a task location network that uses task locations as nodes and task types as connecting edges into a task network, where the nodes represent the task types and the connecting edges represent the relationship between two tasks and the same task location; since it is difficult to rank the importance of the continuous edges in the network, the continuous edges indicating the task points are converted into nodes of the tasks to form the task network l (g).
Furthermore, in step S3, each employee has a task set that it completes, and if the number of tasks of the task type accounts for 10% of the total tasks of the employee, the task type is added to the task set of the employee; and (4) checking the coverage degree of the task type of the employee in the task network, namely the proportion of the task set of the employee in all task types in the task network.
In step S4, calculating the similarity between any two node pairs in each employee task set to measure the difference of each employee task;
the simplest similarity index based on local information is a common neighbor, and if two nodes have a plurality of common neighbor nodes, the two nodes are similar; for node v in the networkxAnd the neighbor set is defined as gamma (x), then v is two nodesxV and vyThe similarity of (c) is defined as the number of neighbors they share, i.e. the number of neighbors
sxy＝|Γ(x)∩Γ(y)|
sxy＝|Γ(x)∩Γ(y)|
On the basis of common neighbors, the influence of node degrees is considered, and a plurality of similarity indexes exist in different ways from different angles:
the Salton index is also called cosine similarity, which is defined as
Figure BDA0001568003030000031
Wherein k isx，kyDegree of a node, representing the number of edges directly connected to the node;
jaccard index defined as
Figure BDA0001568003030000032
The AA index assigns a weight value to each node according to the degree of the common neighbor node, wherein the weight value is equal to one log fraction of the degree of the node, namely the Adamic-Adar index is defined as
Figure BDA0001568003030000033
Resource allocation index takes into account that there are not two nodes v directly connected in the networkxV and vyFrom vxCan deliver some resources to vyIn the process, their mutual neighbors become the medium of transfer; v is given that each medium has a unit of resources and is evenly distributed to its neighborsxThe number of resources that can be received is defined as
Figure BDA0001568003030000034
For a weighted network, the node degree used in the above formula may use the strength of the node;
when the coverage degree of the task types of the employees in the task network is similar, the diversity of the task sets is larger, and the diversity of the employees is stronger.
In the step S5, a preliminary ranking of the diversity of the employees is obtained according to the coverage degree of the employee task set, and the more types of tasks in the employee task set, the more diversity of the tasks is reflected; but when the coverage degree is similar or the same, the difference between the tasks of the staff is larger, and the diversity is relatively more.
The invention is applicable to companies with complete user behavior data like hospital logistics delivery, but without privacy data like user identity. The invention takes the research of logistics transportation data of Zhejiang hospitals as an example, a weighted undirected task place network is converted into a task network, and the diversity ordering of the employees is obtained according to the coverage degree of the employee task types in the task network and the difference of employee task sets.
The invention has the beneficial effects that: the method for analyzing the nodes by applying network science evaluates the diversity of the staff from a network structure and has a good effect.
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FIG. 1 is a flow chart of employee diversity ranking based on a network graph according to an embodiment of the present invention;
FIG. 2 is a diagram of a task site network according to an embodiment of the present invention;
FIG. 3 is a diagram of a task network according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an example of a staff diversity order according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 4, the invention relates to a staff diversity ordering method based on network diagrams, which uses logistics transportation data of Zhejiang hospitals, transforms a weighted undirected task place network into a task network, and obtains the ordering of staff diversity by combining the difference of staff task sets according to the coverage degree of staff task types in the task network.
The invention comprises the following steps:
s1: establishing a weighted undirected network among task sites through the data of the task sites of the staff;
s2: converting the task site network into a weighted undirected task network with task types as nodes;
s3: obtaining a preliminary ranking of the diversity of the employees according to the coverage degree of the task type of each employee in the task network;
s4: calculating the difference of task types of the employees with similar network coverage degrees;
s5: and integrating the network coverage degree and the task difference of the task type of the staff to obtain the diversity final ranking of the staff.
Further, in step S1, the task location in the task data is determined according to the employee' S task locationEstablishing a task site network by the relation between the two parties; the number of the connections between the task sites is different, the number of the tasks between the two sites is used as a continuous edge weight in the task site network, and the established weighted undirected graph is represented as G (V, E, W), wherein V [ upsilon [ ]1,υ2,…,υM]Representing each task location, E ═ E1,e2,…,eN]Indicating that there is a task relationship between task locations, W ═ W1,w2,…,wN]Indicating the number of tasks of each place; according to the obtained task location weighted undirected network, calculating the intensity of each node, and defining the intensity of the node i as
Figure BDA0001568003030000041
Wherein the weighting network G comprises M nodes, and the weight matrix is W ═ Wij)。
Further, in step S2, mapping a task location network that uses task locations as nodes and task types as connecting edges into a task network, where the nodes represent the task types and the connecting edges represent the relationship between two tasks and the same task location; because the importance of the continuous edges is difficult to sequence in the network, the continuous edges representing the task places are converted into nodes of the tasks to form a task network L (G); and analyzing the topological structure of the nodes in the transformed task network to obtain the structural characteristics of different task types.
Furthermore, in step S3, each employee has a task set that it completes, and if the number of tasks of the task type accounts for 10% of the total tasks of the employee, the task type is added to the task set of the employee; the coverage degree of the task type of the employee in the task network is checked, namely the proportion of the task set of the employee to all the task types in the task network is checked to roughly evaluate the diversity of the tasks of the employee, and the diversity is expressed as
Figure BDA0001568003030000042
Wherein the coverageiIndicating the degree of network coverage of employee i, ciNumber of task types representing employee i, cnetRepresenting the total number of tasks of the task network.
In step S4, calculating the similarity between any two node pairs in each employee task set to measure the difference of each employee task;
the simplest similarity index based on local information is a common neighbor, and if two nodes have a plurality of common neighbor nodes, the two nodes are similar; for node v in the networkxAnd the neighbor set is defined as gamma (x), then v is two nodesxV and vyThe similarity of (c) is defined as the number of neighbors they share, i.e. the number of neighbors
sxy＝|Γ(x)∩Γ(y)|
sxy＝|Γ(x)∩Γ(y)|
On the basis of common neighbors, the influence of node degrees is considered, and a plurality of similarity indexes exist in different ways from different angles:
the Salton index is also called cosine similarity, which is defined as
Figure BDA0001568003030000051
Wherein k isx，kyIs the degree of a node, representing the number of edges directly connected to the node.
Jaccard index defined as
Figure BDA0001568003030000052
The AA index assigns a weight value to each node according to the degree of the common neighbor node, wherein the weight value is equal to one log fraction of the degree of the node, namely the Adamic-Adar index is defined as
Figure BDA0001568003030000053
Resource allocation indicator takes into account that there is no direct connection in the networkTwo nodes vxV and vyFrom vxCan deliver some resources to vyIn the process, their mutual neighbors become the medium of transfer; v is given that each medium has a unit of resources and is evenly distributed to its neighborsxThe number of resources that can be received is defined as
Figure BDA0001568003030000054
For a weighted network, the node degree used in the above formula may use the strength of the node;
when the coverage degree of the task types of the employees in the task network is similar, the diversity of the task sets is larger, and the diversity of the employees is stronger.
In the step S5, a preliminary ranking of the diversity of the employees is obtained according to the coverage degree of the employee task set, and the more types of tasks in the employee task set, the more diversity of the tasks is reflected; but when the coverage degree is similar or the same, the difference between the tasks of the staff is larger, and the diversity is relatively more.
As described above, according to the embodiment of the staff diversity ranking method in the hospital logistics transportation system, the weighted undirected task place network is converted into the task network, and the ranking of the staff diversity is obtained according to the coverage degree of the staff task types in the task network and the difference of the staff task sets. The diversity ranking of the logistics transport staff of the hospital is measured from the network topology structure, and the requirement of actual use is met. The present invention is to be considered as illustrative and not restrictive. It will be understood by those skilled in the art that various changes, modifications and equivalents may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (4)

1. A staff diversity ordering method based on a network diagram is characterized in that: the sorting method comprises the following steps:
s1: establishing a weighted undirected network among task sites through the data of the task sites of the staff;
s2: converting the task site network into a weighted undirected task network with task types as nodes;
s3: obtaining a preliminary ranking of the diversity of the employees according to the coverage degree of the task type of each employee in the task network;
s4: calculating the difference of task types of the employees with similar network coverage degrees;
s5: integrating the network coverage degree and task difference of the task types of the employees to obtain the diversity final ranking of the employees;
in step S1, a task place network is established according to the relation between task places in the employee task data; the number of the connections between the task sites is different, the number of the tasks between the two sites is used as a continuous edge weight in the task site network, and the established weighted undirected graph is represented as G (V, E, W), wherein V [ upsilon [ ]1,υ2,…,υM]Representing each task location, E ═ E1,e2,…,eN]Indicating that there is a task relationship between task locations, W ═ W1,w2,…,wN]Indicating the number of tasks of each place; according to the obtained task location weighted undirected network, calculating the intensity of each node, and defining the intensity of the node i as
Figure FDA0003151394820000011
Wherein the weighting network G comprises M nodes, and the weight matrix is W ═ Wij)；
In step S2, a task place network with task places as nodes and task types as connecting edges is mapped as a task network, where a node represents a task type and a connecting edge represents that two tasks have a relationship with the same task place; since it is difficult to rank the importance of the continuous edges in the network, the continuous edges indicating the task points are converted into nodes of the tasks to form the task network l (g).
2. The employee diversity ranking method based on network graph as claimed in claim 1, characterized in that: in step S3, each employee has a task set that it completes, and if the number of tasks of the task type accounts for 10% of the total tasks of the employee, the task type is added to the task set of the employee; and (4) checking the coverage degree of the task type of the employee in the task network, namely the proportion of the task set of the employee in all task types in the task network.
3. The employee diversity ranking method based on network graph as claimed in claim 1, characterized in that: in step S4, calculating the similarity between any two node pairs in each employee task set to measure the difference of each employee task;
the simplest similarity index based on local information is a common neighbor, and if two nodes have a plurality of common neighbor nodes, the two nodes are similar; for node v in the networkxAnd the neighbor set is defined as gamma (x), then v is two nodesxV and vyThe similarity of (c) is defined as the number of neighbors they share, i.e. the number of neighbors
sxy＝|Γ(x)∩Γ(y)|
On the basis of common neighbors, the influence of node degrees is considered, and a plurality of similarity indexes exist in different ways from different angles:
the Salton index is also called cosine similarity, which is defined as
Figure FDA0003151394820000021
Wherein k isx，kyDegree of a node, representing the number of edges directly connected to the node;
jaccard index defined as
Figure FDA0003151394820000031
The AA index assigns a weight value to each node according to the degree of the common neighbor node, wherein the weight value is equal to one log fraction of the degree of the node, namely the Adamic-Adar index is defined as
Figure FDA0003151394820000032
Resource allocation index takes into account that there are not two nodes v directly connected in the networkxV and vyFrom vxCan deliver some resources to vyIn the process, their mutual neighbors become the medium of transfer; v is given that each medium has a unit of resources and is evenly distributed to its neighborsxThe number of resources that can be received is defined as
Figure FDA0003151394820000033
For a weighted network, the node degree used in the above formula may use the strength of the node;
when the coverage degree of the task types of the employees in the task network is similar, the diversity of the task sets is larger, and the diversity of the employees is stronger.
4. The employee diversity ranking method based on network graph as claimed in claim 1, characterized in that: in the step S5, a preliminary ranking of the diversity of the employees is obtained according to the coverage degree of the employee task set, and the more types of tasks in the employee task set, the more diversity of the tasks is reflected; but when the coverage degree is similar or the same, the difference between the tasks of the staff is larger, and the diversity is relatively more.
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