CN108510205B - A hypergraph-based method for assessing author skills - Google Patents

A hypergraph-based method for assessing author skills Download PDF

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CN108510205B
CN108510205B CN201810316651.XA CN201810316651A CN108510205B CN 108510205 B CN108510205 B CN 108510205B CN 201810316651 A CN201810316651 A CN 201810316651A CN 108510205 B CN108510205 B CN 108510205B
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夏锋
杨安东
刘雷
孔祥杰
于硕
宁兆龙
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Dalian University of Technology
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Abstract

本发明属于学者技能评估技术领域，涉及一种基于超图的学者技能评估方法，可以细粒度的评估学者在某一领域某一技能的水平，且可以体现学者技能随时间的变化规律。该方法考虑了论文数目及论文质量、不同领域的差异性、时间变化等因素。超图概念的使用使本方法可以融合学者、领域和技能，从而使本方法提供了一种细粒度的评估方案。在计算学者、领域和技能的距离时，以论文引用量、H‑index等传统评估参数为基础进行拓展，保证了可靠性，同时归一化的使用提高了运算效率，降低了误差。最后加入时间因素使本方法可以分析学者领域，技能随时间的变化，为研究提供了更多的“原材料”。

Figure 201810316651

The invention belongs to the technical field of scholar skill assessment, and relates to a hypergraph-based scholar skill assessment method, which can fine-grainly assess the level of a scholar's skill in a certain field and can reflect the changing law of the scholar's skill over time. The method takes into account factors such as the number and quality of papers, differences in different fields, and time changes. The use of the hypergraph concept allows the method to integrate scholars, domains and skills so that the method provides a fine-grained evaluation scheme. When calculating the distance between scholars, fields and skills, it is expanded based on traditional evaluation parameters such as paper citations and H-index to ensure reliability. At the same time, the use of normalization improves computing efficiency and reduces errors. Finally, adding the time factor enables this method to analyze the change of scholars' fields and skills over time, providing more "raw material" for research.

Figure 201810316651

Description

Author skill evaluation method based on hypergraph
Technical Field
The invention belongs to the technical field of author skill evaluation, and relates to an author skill evaluation method based on a hypergraph.
Background
With the continuous development of science and technology, more and more authors engaged in scientific research work, and the research on scientific researchers is promoted by the increase of the number of scientific researchers. The system and the method have the advantages that the system and the method can evaluate the level of a scientific research worker, have the specialties, have the rules and the like published in a thesis, have the promotion effects on the establishment of a scientific research team, project investment, the comprehensive evaluation of academic levels of authors, the comparison of different authors, the study of academic cooperation behavior mechanisms, the discovery of potential rules of scientific research and the like, and are beneficial to the progress and development of academic circles and even human society.
Currently, H-index is mostly used for evaluating the author level, and indexes such as quoted number of papers and publication number of papers are adopted. The above indexes are generally used for overall evaluation of an author, and have the problems that the proficiency of a certain skill of the author cannot be known, the academic level change of the author in a certain time period cannot be known, and the like, so that the research range and depth are limited to a certain extent.
A hypergraph is a generalized graph in which a hyperedge may contain multiple vertices. The characteristics of the super-edge enable the hypergraph to be capable of fusing the multiple attributes of the author, and the hypergraph is quite suitable for the three-element processing of the author, the field and the skill required in the skill research of the author.
Disclosure of Invention
The invention mainly aims at the defects of the existing research, provides an author skill evaluation method, and provides an author evaluation algorithm based on a hypergraph by analyzing the contribution of an author in published papers and combining time factors. The algorithm carries out fine-grained evaluation on the author, considers the number and quality of the treatises and also considers the differences of different fields, the proficiency of the author in a specific skill in a certain field can be obtained through the algorithm, and meanwhile, the time factor is added, so that the change of the skill of the author along with the time can be obtained.
The technical scheme of the invention is as follows:
an author skill assessment method based on hypergraphs comprises the following steps:
step 1): combining the skill of the author and the author in the paper and the field of the paper into a super limit, counting all papers participated by the author, merging the skill types, and obtaining the statistical data of the author, the skill and the field of the papers; the hypergraph has good compatibility, and can integrate three factors of an author, a field and skill; counting the thesis information published by the author, wherein the author, the skill and the field are used as three vertexes of the super edge;
the super edge connects a certain skill of an author in a certain field, the proficiency of the skill of the author in the certain field is reflected by calculating the weight of the super edge, and the network scale of the author can be effectively reduced by using the super graph;
the skills are complicated, so that the related calculation of the following steps can be influenced, and the skills in the data set are merged to obtain a uniform standard data set;
step 2): combining the three vertexes of the hyperedge pairwise, and calculating the distance of each vertex in the hyperedge; the distance between the attributes is calculated by the following formula:
distance of author j from field f:
Figure GDA0003096019640000021
where n is the number of authors in the field, nfIs the total number of papers in the field, ciIs the quoted number, h, of paper ijIs the H-index of the author;
the distance between the author j and the field f is normalized, so that subsequent data processing is facilitated; the normalized formula is as follows:
Figure GDA0003096019640000022
wherein avg (dis (field)) refers to the distance between all authors and the field, and the calculation results are summed and averaged;
distance of author j from skill s:
Figure GDA0003096019640000031
wherein n is the number of characters used by the author in the skill, ciIs the number of times of citation, h, of paper ijIs the H-index, n, of the authorisIs the number of participants in the skill in the paper;
the distance of author j from skill s uses the following normalization formula:
Figure GDA0003096019640000032
wherein, avg (dis) refers to the distance between all authors and skills, and the average value is calculated after the results are summed;
distance of area f from skill s:
Figure GDA0003096019640000033
wherein n isfIs the total number of papers in the field, nsIs the total number of papers containing the skill, nfsIs the number of domains that contain the skill;
the distance of the domain f from the skill s is normalized using the following formula:
Figure GDA0003096019640000034
wherein, avg (field, skip) refers to the average value after summing the distance calculation results of all fields and skills;
step 3): calculating the weight of the excess edge by using an excess edge weight calculation method, wherein the weight is the proficiency of a writer in a specific skill in a certain field; calculating the weight of the super edge by using the deformation of the Gaussian kernel function according to the hypergraph theory, and linking the three distances in the step 2) to obtain a specific skill level parameter of the author in a certain field;
the excess edge weight is calculated using the following formula:
Figure GDA0003096019640000041
wherein d (x, y) is the distance between two authors, areas and skills, σ is the average for the distance;
Figure GDA0003096019640000042
is the level value of the skill of i author in the field f s;
step 4): the process is changed along with time, and is applied to each year, so that the change rule of the specific skill of an author in a certain field along with the change of the time is obtained; various time points exist in the research life of an author, such as changing a research institution and changing the research direction, and if the change condition of each skill of the author at different time is known, the change rule of the skill of the author along with the time can be researched, so that the potential rule of scientific research is discovered; in order to realize the goal, the data set is divided into a subdata set every year according to the increase of time, and the data in the year and before the year are stored; and repeating the step 2) and the step 3) for each data subset, and extracting the skill change of each author in each year from the result, namely obtaining the change condition of the skill of the author along with the time.
The invention has the beneficial effects that: skill assessment of authors is a hypergraph-based method that takes into account the number of papers and the quality of the papers, differences in different fields, temporal variations, etc. The use of hypergraph concepts allows the method to fuse authors, fields and skills, thus allowing the method to provide a fine-grained assessment solution. When the distance of an author, a field and skill is calculated, expansion is carried out on the basis of traditional evaluation parameters such as paper quoted amount, H-index and the like, reliability is guaranteed, meanwhile, operation efficiency is improved by normalization, and errors are reduced.
The invention adds time factor to analyze the author field, and the skill changes with time, to provide more raw material for follow-up research.
Drawings
FIG. 1 is a flow chart of the data preprocessing performed on Ploss datasets according to experimental requirements in accordance with the present invention.
Fig. 2 is a final result author skill radar chart a of the present invention.
FIG. 3 is a final result author skill radar chart b of the present invention
Fig. 4 is a schematic diagram a of the annual maximum skill level of the author as it changes incrementally over time.
Fig. 5 is a schematic diagram b of the author's annual maximum skill level increasing with time.
FIG. 6 is an exemplary graph of author skill over time.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below.
The embodiment of the invention provides an author skill evaluation method based on a hypergraph, which comprises the following steps:
step 1: selecting a Plosone data set as an experimental data set of the method, and preprocessing the Plosone data set, wherein the processing process is shown in fig. 1.
In order to capture the contribution of the author in the paper, i.e. the skill the author uses in this paper, the present invention uses the Plosone dataset. Data set raw data are as follows:
TABLE 1 Ploss dataset
Figure GDA0003096019640000051
As can be seen from table 1, the number of different skills is very large, which may be due to the lack of standard naming rules for the skills, resulting in similar skills being used with different expressions. The original skill naming if the data set is used directly can lead to inaccurate and redundant results.
Therefore, the invention makes statistics on skills, finds that 10342 skills appear less than 10 times and 21 skills appear in a jump increase mode, therefore, the skills appear less than 10 times are discarded, and then classifies the skill names based on the 21 jump increase skills to finally obtain 16 skill classes, wherein each skill class is represented by the skill name with the largest occurrence frequency.
Because the number of authors is large, there is a great probability that different authors have the same name, which may interfere with the experimental results. In order to relieve the influence of the same-name problem on the experimental result, the method disclosed by the invention is used for carrying out same-name distinguishing on the author by combining the actual condition that the data set contains the mechanism to which the author belongs and using the cooperation condition of the author and the research mechanism to which the author belongs as reference according to the existing same-name distinguishing algorithm. The homonymous distinguishing rule used by the invention is as follows: if two authors of the same name have collaborated with the same author, it is reasonable to think that the two authors of the same name have the same probability. If two authors of the same name belong to the same research institution, the two authors of the same name are likely to be the same person. Because the same-name distinguishing is a research problem at present, no better solution exists, and the invention does not discuss the same-name distinguishing problem any more.
Step 2: combining the three vertexes of the hyper-edge two by two, and then calculating the distance between the three vertexes.
And (3) naming the standardized skills obtained in the step (1) and collecting the data subjected to homonymy distinguishing, calculating the data according to a distance calculation formula, and then normalizing the result to obtain the distances between authors and fields, between authors and skills and between skills and fields.
And step 3: and calculating the weight of the excess edge through a weight calculation method of the excess edge, wherein the weight is the specific skill proficiency of an author in a certain field.
According to the hypergraph theory, the deformation of the Gaussian kernel function is used for calculating the hyperedge weight, and the calculation formula is as follows:
Figure GDA0003096019640000061
wherein d (j, s) represents the distance between the author and the skill, d (j, f) represents the distance between the author and the domain, d (f, s) represents the distance between the domain and the skill, and σ representsjsMean, σ, representing the distance of all authors from the skilljfMean value, σ, representing the distance of all authors from the fieldfsRepresents the average of all domain-to-skill distances.
To demonstrate the author skill distribution in a concrete and concise manner, the present invention uses radar maps to represent the skill distribution of the author. Fig. 2 and 3 give radar map examples of the author skill distributions, one field for each circle in the figures.
And 4, step 4: the change rule of the specific skill of the author in a certain field along with the change of the time can be obtained by applying the process along with the change of the time to each year.
According to the invention, the data of Ploss is divided into one data set every year according to the increment of time, and 12 sub-data sets from 2006 to 2017 are divided.
And (3) applying the steps 2 and 3 to the data subset of each year, and extracting the skill change condition corresponding to each author in different years from the result, so that the change of different skills of the author in different fields along with time can be obtained.
The invention integrates the author skill and year into a line graph in order to show the change of the author skill along with the time. Figure 6 gives the author a line graph of skill over time. Since the number of skills is too large, each learner has a plurality of skill levels which change with time, and it is difficult to find rules, the extraction of the highest skill level of the author is combined with the time variation, and the scatter diagrams shown in fig. 4 and 5 are obtained.

Claims (1)

1.一种基于超图的作者技能评估方法，其特征在于，步骤如下：1. a method for assessing author skills based on hypergraph, is characterized in that, step is as follows: 步骤1)：将作者、作者在此论文中的技能和此论文的领域组成超边，统计作者参与的所有论文，归并技能种类，得到论文作者、技能和领域的统计数据；将作者发表的论文信息进行统计，作者、技能和领域作为超边的三个顶点；Step 1): Combine the author, the author's skills in this paper and the field of this paper into a super-edge, count all papers the author participates in, merge the skill types, and get the statistical data of the author, skill and field of the paper; combine the papers published by the author Information is counted, with author, skill and domain as the three vertices of the hyperedge; 步骤2)：将超边的三个顶点两两结合，计算超图中各个顶点的距离；各个属性间的距离由以下公式计算：Step 2): Combine the three vertices of the hyperedge in pairs to calculate the distance of each vertex in the hypergraph; the distance between each attribute is calculated by the following formula: 作者j与领域f的距离：The distance between author j and domain f:
Figure FDA0003096019630000011
Figure FDA0003096019630000011
 其中，n是作者在该领域的论文数，nf是该领域的论文总数，ci是论文i的被引用数量，hj是作者的H-index；Among them, n is the number of papers of the author in the field, n f is the total number of papers in the field, ci is the number of citations of paper i , and h j is the H-index of the author; 对作者j与领域f的距离进行归一化处理，方便进行后续数据处理；归一化的公式如下：The distance between author j and domain f is normalized to facilitate subsequent data processing; the normalization formula is as follows:
Figure FDA0003096019630000012
Figure FDA0003096019630000012
 其中，avg(dis(author,field))指对所有作者与领域的距离，计算结果加和后求平均值；Among them, avg(dis(author,field)) refers to the distance between all authors and the field, and the calculation results are summed and averaged; 作者j与技能s的距离：The distance between author j and skill s:
Figure FDA0003096019630000013
Figure FDA0003096019630000013
 其中，n是作者用该技能的论文数，ci是论文i的被引用次数，hj是作者的H-index，nis是该论文中这项技能的参与人数；Among them, n is the number of papers that the author uses this skill, ci is the number of citations of paper i , h j is the H-index of the author, and n is the number of participants of this skill in the paper; 作者j与技能s的距离使用如下归一化公式：The distance between author j and skill s is normalized using the following formula:
Figure FDA0003096019630000014
Figure FDA0003096019630000014
 其中，avg(dis(author,skill))指对所有作者与技能的距离，计算结果加和后求平均值；Among them, avg(dis(author,skill)) refers to the distance between all authors and skills, and the calculation results are summed and averaged; 领域f与技能s的距离：The distance between domain f and skill s:
Figure FDA0003096019630000021
Figure FDA0003096019630000021
 其中，nf是该领域的论文总数，ns是含有该技能的论文总数，nfs是含有该技能的领域数；where n f is the total number of papers in the field, n s is the total number of papers with the skill, and n fs is the number of fields with the skill; 领域f与技能s的距离使用如下归一化公式：The distance between domain f and skill s is normalized using the following formula:
Figure FDA0003096019630000022
Figure FDA0003096019630000022
 其中，avg(dis(field,skill))指对所有领域与技能的距离计算结果加和后求平均值；Among them, avg(dis(field,skill)) refers to the average of the distance calculation results of all fields and skills; 步骤3)：通过超边的权重计算方法，计算超边的权重，权重即为一个作者在某一领域的特定技能熟练度；根据超图理论，使用高斯核函数的变形计算超边的权重，将步骤2)中的三个距离链接，得出作者在某一领域的技能水平参数；Step 3): Calculate the weight of the hyperedge through the weight calculation method of the hyperedge, and the weight is the specific skill proficiency of an author in a certain field; according to the hypergraph theory, use the deformation of the Gaussian kernel function to calculate the weight of the hyperedge, Link the three distances in step 2) to obtain the author's skill level parameter in a certain field; 超边权重使用如下公式计算：The hyperedge weights are calculated using the following formula:
Figure FDA0003096019630000023
Figure FDA0003096019630000023
 其中，d(x,y)为作者、领域和技能两两之间的距离，σ为对于距离的平均值；
Figure FDA0003096019630000024
为i作者在f领域s技能的水平值；Among them, d(x,y) is the distance between authors, fields and skills, and σ is the average of the distances;
Figure FDA0003096019630000024
is the level value of the s skill of the i author in the f field; 步骤4)：将上述过程随着时间变化，应用于每一年份，即得到作者在某一领域的特定技能随着时间变化的变化规律；在作者的研究生涯中存在各种时间点，如更换研究机构和改变研究方向，如果知晓作者在不同时间各个技能的变化情况，便研究作者技能随时间变化规律，从而发现科学研究的潜在规律；为实现这一目标，将数据集根据时间的增长，每年划分出一个子数据集，存储本年及本年以前的数据；对每个数据子集重复上述步骤2)和步骤3)，在结果中提取每个作者每一年的技能变化，即得到作者技能随时间变化的情况。Step 4): Apply the above process over time and apply it to each year to obtain the changing law of the author's specific skills in a certain field over time; there are various time points in the author's research career, such as changing Research institutions and changes in research direction, if you know the changes of authors' skills at different times, they can study the changes of authors' skills over time, so as to discover the potential laws of scientific research; to achieve this goal, the data set will grow according to time, Divide a sub-data set every year to store the data of this year and before this year; repeat the above steps 2) and 3) for each data subset, and extract the skill changes of each author in each year from the results, that is, get Changes in author skills over time.
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