CN109657122B - Academic team important member identification method based on academic big data - Google Patents
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Abstract
The invention discloses an academic team important member identification method based on academic big data, which is used for identifying a team leader from the aspects of degree centrality, H factor and academic age, and evaluating and marking team key members by using a DHRank model based on a PageRank method for the first time. In order to reasonably perform team clustering, after the leader-centered shortest path-based team clustering is performed, the clustering result is further improved by using a method based on team scale comparison and leader H factor comparison, and the accuracy of the result of identifying key members by using a DHrank model in the later period is improved. Compared with other methods, the method provided by the invention is more accurate in finding influential learners and has lower computation time complexity. The invention provides a new method for identifying the leader and the key in the academic team, and provides a new solution for scientific research of the team in the academic network.
Description
Technical Field
The invention belongs to the technical field of academic network identification, and relates to an academic team important member identification method based on academic big data, in particular to a multi-feature leader node identification method based on degree centrality, H factor and academic age and key member identification based on a PageRank improved DHRank algorithm.
Background
With the diversification of scientific research today, academic teams are evolving towards a trend across universities, research centers and even research areas. The popularity and complexity of scientific research collaboration has led to an ever-increasing size of academic teams, and increasingly complex relationships between team members and between teams. The important members of the team are the pillars of the team, and the important members not only take on the planning management responsibility in the team but also take on the responsibility of the whole team for communicating and cooperating with the outside. Research on important members of a team can better reflect the cooperative structure and development trend of the team. However, the difficulty of identifying important team members is increased due to the increase of academic network scale and the complexity of cooperative relations, most of current researches only aim at identifying teams and team leaders, and the problem of identifying the important team members is not solved comprehensively and accurately. Therefore, researchers are required to provide a reasonable and accurate method for identifying important academic team members to better study the structure and composition relationship of the academic team.
Disclosure of Invention
Aiming at the defects of the existing research, the invention provides a method for identifying a team leader and important members by combining indexes such as the utilization centrality, the H factor, the academic age of a scholars and the like with a DHrank algorithm. The method identifies the leader of a team from the perspective of a macro network structure, academic influence of a student and research efficiency of the student; the team leader is used as the center to conduct the team construction based on the shortest path, the DHrank algorithm improved based on the Pagerank algorithm is used for evaluating the capacity of the members in the team, and the key members in the team are obtained to be used for researching academic team composition and predicting the future team leader. The present invention provides a new solution to the problem of identifying influential leader nodes and key members in an academic team.
The technical scheme of the invention is as follows:
an academic team important member identification method based on academic big data comprises the following steps:
step 1): calculating the age of a target window by counting the real citation condition of the conference paper, and establishing an academic network according to the student paper in the age of the target window;
step 2): carrying out statistical calculation on the degree centrality of the scholars in the academic network and the H factor of the scholars by utilizing the connection condition of the scholars nodes in the academic network constructed in the step 1) and the real reference amount of the scholars paper to obtain a potential team leader list;
step 3): counting the publication situation of a student thesis in an academic network, calculating the academic age of the student, determining a leader node by using the academic age and the potential team leader list obtained in the step 2), and dividing a non-leader node into academic teams belonging to different leaders by using the shortest path between the leader node and the leader;
step 4): identifying and marking key members in the academic team by using a DHrank algorithm according to the academic team obtained in the step 3), and obtaining important members in the academic team, including a leader and the academic team key members.
The steps 1) to 4) are as follows:
step 1):
through statistical analysis of the publication of papers in the DBLP dataset and the cooperation of scholars, the identification and analysis of important team members with 10 years as the target window age is determined. The treatise can be regarded as concrete representation of the cooperative relationship between scholars, and the academic network based on the treatise can truly reflect the cooperation condition and the team attribution of the scholars. And after the target age is determined, establishing an academic network by using the student thesis information in the target age. The nodes in the academic network represent scholars, the edges represent the cooperative relationship between the scholars, and the weights of the edges represent the number of cooperative papers of the two scholars.
The step 2) comprises the following three steps:
2.1) calculating the degree centrality of a scholars in the academic network, and calculating the degree centrality of the scholars according to the binding relationship between the scholars and other scholars in the academic network and the number of nodes in the academic network; the degree centrality is the most direct measurement index for describing the importance degree of the node in network analysis, and the larger the degree of one node is, the higher the degree centrality of the node is, and the more important the node is in the network; the importance and the influence of a scholars in an academic collaboration team can be measured according to the centrality of the degree, and the calculation formula of the centrality of the degree is as follows:
here, deg (a, G) represents the number of all the coworkers of the student a in the academic network G, and a represents the number of all the students in the academic network.
2.2) calculating the H factor of the scholars; the H factor is also called H index, wherein 'H' represents the high citation times of a scholars, and the H factor of a scholars means that the scholars have at most H papers which are cited at least H times respectively. The H factor can accurately reflect the academic achievement of a person, and the higher the H index of a scholars is, the more influential the thesis of the scholars is. According to the definition of the H factor, the real quoted amount published by the statistical paper is calculated for the H factor of the scholars in the academic network.
And 2.3) finding out a learner with two indexes higher than the average value of the indexes according to the two indexes of the centrality and the H factor of the learner obtained by calculation and statistics, and marking the learner as a potential team leader node.
The step 3) comprises the following two steps:
3.1) calculating the academic age of the scholars: the academic age of each scholarer in the academic network, namely the time span from the publication of the first paper to the publication of the last paper, can be obtained according to the publication condition of the papers. Statistics are made on the papers publication behavior of scholars, who are less than 5 academic ages can be generally considered student researchers, and these people cannot be said to be true team leaders. Screening out scholars with academic ages greater than or equal to 5 and marking the scholars as leader nodes on the basis of the potential team leader list obtained in the step 2).
3.2) for a non-leader node in the academic network, calculating the shortest path distance from the node to all the leader nodes, wherein the leader node with the shortest path distance is the leader node of the node, and dividing the node into teams corresponding to the leader node. After team division, some nodes belong to a plurality of teams, so further team division is needed; if more common nodes exist in the two teams, namely the number of the common nodes is larger than the average team node number, the H factors of the leader nodes of the two teams need to be compared, and the leader node with the higher H factor is used as the leader node of all the nodes of the two teams.
The step 4) comprises the following two steps:
4.1) calculating the degree centrality of all members in the team in the academic network, then calculating the sum of the degree centrality and the H factor of each student by combining the H factor calculated in the step 2), and measuring the academic influence of the students;
4.2) calculating the DHrank values of all members in the team in the following way:
wherein, DHR (a)i) Show the scholars aiDHRank value of (A), PR (a)i) Is a scholarsiPageRank value of Deg (a)i) Show the scholars aiDegree in academic networks, hindex (a)i) Is a scholarsiH factor value of (c).
Wherein the scholars a in the academic networkiThe PageRank value is calculated as follows:
wherein E represents the set of all edges in the academic network; (a)i,aj) Representing a student node aiAnd ajConnecting edge between, PR (a)j) Representing a scholars a in a networkiCollaborator ajThe value of the PageRank of (c),
representing a student node ajDegree of (c).
Let P be (PR)1,PR2,…PRn)TFor an n-dimensional PageRank vector, A is the corresponding transition matrix for the academic network, as follows:
wherein A (a)i,aj) Representing the slave node aiTo student node ajThe transition probability of (a) is,
representing a student node aiDegree of (c).
The initial iteration value of the PageRank value is designated as 1 by using the poweriteration method. Since the co-literary relationship in academic networks is a two-way relationship and does not consider the case of single-author published papers, the PageRank minimum is set to 0 when performing the iteration. Solving through multiple rounds of iteration:
Pk＝ATPk-1
finally converging to Pk-Pk-1And | | is less than xi, namely the difference value of two iterations is less than the threshold xi. k denotes the number of iterations at the end of an iteration.
In the present invention, threshold ξ ═ 10-5。
After the DHRank value of each member in the team is obtained, the members in the team are arranged in a descending order according to the DHRank value, and the criticality of the members in the team is in direct proportion to the DHRank value.
The invention has the beneficial effects that: the invention utilizes the characteristics of the degree centrality, the H factor and the academic age of the scholars in the academic network to identify the leaders in the academic team, and provides a new team construction method by using a method based on the shortest path. In order to more accurately and effectively find the key members in the team, the invention also provides an improved DHrank algorithm based on the Pagerank to identify the key members in the team. The experimental result shows that the method provided by the invention is more accurate in finding influencers, and compared with other methods, the method has lower operation time complexity. The invention provides a new method for identifying the leader and the key in the academic team, and provides a new solution for scientific research of the team in the academic network.
Drawings
Fig. 1 shows the distribution of academic ages of scholars and the distribution of the ratio of the number of scholars with a certain academic age in a predetermined cooperation window.
Fig. 2 and 3 show degree distribution of scholars and H-factor distribution of scholars in the academic network, respectively.
Fig. 4 shows academic age distribution in the academic network, and the publication age of the papers screened for potential leaders can be determined to be 5 years according to the distribution.
FIG. 5 is a diagram of the situation of common team members existing between different teams during team identification.
FIG. 6 is the result of a partial team identification, noted in the figure as the leader of the team.
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 academic team important member identification method based on academic big data, which comprises the following steps:
step 1: and (3) constructing an academic network by using real thesis co-written data among the scholars through statistically analyzing the thesis publication condition in the DBLP data set and the cooperation condition of the scholars. The nodes in the network represent scholars, and the edges represent the cooperative relationships among the scholars. The cooperation behavior between the scholars is analyzed by a mathematical statistics method through the statistical analysis of the cooperation condition between the scholars to find the cooperation window age.
The invention selects the thesis data in the DBLP data set for experiment, wherein the DBLP is an authoritative data set in the field of computers, and collects the relevant information of more than 400 million thesis, and the data set is screened and filtered when the effective data used for the experiment is acquired from the data set. The DBLP dataset was preprocessed to remove papers from only one author and all papers from which the academic age was less than 5. The invention carries out team construction on the basis of the co-culture relationship, and considers that the co-culture paper is the concrete embodiment of the co-culture relationship, so that only one author's paper can not reflect the real cooperation condition of the author; meanwhile, scholars with academic ages less than 5 have little experience in the academic field and are generally considered as student researchers, and cannot become a leader or key member of a relatively large academic team. Fig. 1 shows the distribution of academic ages of scholars and the distribution of the ratio of scholars with a certain academic age in a predetermined cooperation window. Fig. 4 shows academic age distribution in the academic network, and the publication age of the papers screened for potential leaders can be determined to be 5 years according to the distribution.
Step 2: and (4) utilizing the real reference quantity of the academic network and the data set paper to carry out statistical calculation on the centrality and the H factor of the scholars in the academic network and generate a potential team leader list.
2.1) calculating the centrality of the learner in the academic network, only considering the cooperative relationship among the learners, and calculating the centrality of the learner according to the cooperative relationship between a certain learner and other learners in the academic network and the number of nodes in the academic network. The calculation formula of the centrality of the degree is as follows:
here, deg (a, G) represents the number of all the coworkers of the student a in the academic network G, and a represents the number of all the students in the academic network.
2.2) the H factor of the scholars in the academic network can be calculated according to the real quoted amount published by the paper.
2.3) finding out a learner with two indexes higher than the average value of the indexes according to the two indexes of the centrality of the degree of the learner and the H factor obtained by calculation and statistics, and marking the learner as a potential team leader node, wherein the experimental result shows that the average value of the degree of the learner is 7.8181, the average value of the H factor is 3.2178, and the distribution of the centrality of the degree and the H factor is shown in fig. 2 and 3.
And step 3:
and (3) counting the academic age of the scholars in the academic network according to the publication condition of the papers in the data set, and determining the true leader node in the potential leader node list obtained in the step 2. And performing team construction by using the obtained real leader node as a center and using a shortest path-based team clustering method, and after the preliminary construction is completed, performing further team construction on a team with more public nodes to finally obtain a final result of the team construction.
3.1) obtaining the academic age of each student in the academic network according to the publication condition of the paper, finding out students which have been researched for years in the academic scientific research field and have frequently published papers by calculating the index, and further screening the potential leader sections screened in the step 2 by utilizing the condition. Wherein scholars with academic ages greater than or equal to 5 are marked as true leader nodes.
3.2) for non-leader nodes in the academic network, calculating the shortest path distances from the node to all the leader nodes, wherein the leader node with the minimum shortest path distance in the set is the leader node of the node, and dividing the node into teams corresponding to the leader nodes. After the team division is performed, there is a case where some nodes belong to a plurality of teams, and further division processing is performed for this case. If more common nodes exist in the two teams, namely the number of the common nodes is larger than the average team node number, the H factors of the leader nodes of the two teams need to be compared, and the leader node with the higher H factor is used as the leader node of all the nodes of the two teams. The situation of common team members existing between different teams in the team identification process is shown in fig. 5.
Through the steps, the leader and member distribution of all academic teams can be obtained.
And 4, step 4:
in an academic team, not only is only the leader managing the entire team, but there are also key members to assist the leader in managing the entire team. This step is therefore to identify and tag key members of the team using the resulting team and the PageRank algorithm-based DHRank algorithm.
4.1) calculating the degree centrality of all members in the team in the academic network, and then calculating the sum of the degree centrality and the H factor of each student by combining the calculated H factor to measure the academic influence of the students.
4.2) calculate PageRank values for all members of the team, scholar aiThe PageRank value is calculated as follows:
wherein E represents the set of all edges in the academic network; (a)i,aj) Watch (A)Demonstrator node aiAnd ajConnecting edge between, PR (a)j) Representing a scholars a in a networkiCollaborator ajThe value of the PageRank of (c),
representing a student node ajDegree of (c). Let P be (PR)1,PR2,…PRn)TFor an n-dimensional PageRank vector, A is the corresponding transition matrix for the academic network, as follows:
wherein A (a)i,aj) Representing the slave node aiTo student node ajThe transition probability of (a) is,
representing a student node aiDegree of (c).
The initial iteration value of the PageRank value is designated as 1 by using the poweriteration method. Since the co-literary relationship in academic networks is a two-way relationship, and the inventors do not consider the case where a single author published a paper here, the PageRank minimum is set to 0 when performing iterations. Solving through multiple rounds of iteration:
Pk＝ATPk-1
finally converging to Pk-Pk-1And | | is less than ξ, namely the difference of two iterations is less than a certain threshold. In the present embodiment, threshold ξ ═ 10-5。
4.3) calculating the DHrank values of all members in the team in the following way:
wherein, DHR (a)i) Show the scholars aiDHRank value of (A), PR (a)i) Is a scholarsiPageRank value of Deg (a)i) Show the student aiAt the level of the nodes in the academic network,hindex(ai) Is the scholar H factor value.
4.4) obtaining the DHrank value of each member in the team, and then arranging the members in a descending order according to the value, wherein the criticality of the members is in direct proportion to the DHrank value. Key members in a team may help a researcher to be certain in a certain team, and over time, a leader in the team may be replaced by a certain key member. FIG. 6 is the result of a partial team identification, noted in the figure as the leader of the team.
Claims (2)
1. An academic team important member identification method based on academic big data is characterized by comprising the following steps:
step 1): calculating the age of a target window by counting the real citation condition of the conference paper, and establishing an academic network according to the student paper in the age of the target window;
through statistical analysis of thesis publication conditions in the DBLP data set and cooperation conditions among scholars, identification and analysis of important team members are determined by taking 10 years as target window years; establishing an academic network by using the thesis information of the scholars in the target years; nodes in the academic network represent scholars, edges represent the cooperative relationship between the scholars, and the weight of the edges represents the number of cooperative papers of the two scholars;
step 2): carrying out statistical calculation on the centrality of the scholars in the academic network and the H factor of the scholars by utilizing the connection condition of the scholars nodes in the academic network constructed in the step 1) and the real reference amount of the papers of the scholars to obtain a potential team leader list;
2.1) calculating the degree centrality of the scholars in the academic network; calculating the degree centrality of a student according to the binding relationship between the student and other students in the academic network and the number of nodes in the academic network; the calculation formula of the centrality of the degree is as follows:
wherein deg (c) isaG) representsLearning in academic network GaA represents the number of all scholars in the academic network;
2.2) calculating the H factor of the scholars; the H factor of a scholars means that the scholars have at most H papers which are respectively quoted at least H times; according to the definition of the H factor, counting the real quoted quantity published by the thesis to calculate the H factor of the scholars in the academic network;
2.3) finding out a learner with two indexes higher than the average value of the indexes according to the two indexes of the degree centrality and the H factor of the learner obtained by calculation and statistics, and marking the learner as a potential team leader node;
step 3): counting the publication situation of a student thesis in an academic network, calculating the academic age of the student, determining a leader node by using the academic age and the potential team leader list obtained in the step 2), and dividing a non-leader node into academic teams belonging to different leaders by using the shortest path between the leader node and the leader;
3.1) calculating the academic age of the scholars: counting the thesis publication behaviors of the scholars, screening the scholars with the academic age of more than or equal to 5 on the basis of the potential team leader list obtained in the step 2), and marking the scholars as leader nodes;
3.2) for non-leader nodes in the academic network, calculating the shortest path distance from the node to all the leader nodes, wherein the leader node with the shortest path distance is the leader node of the node, and dividing the node into teams corresponding to the leader node; if the number of common nodes in the two teams is larger than the average team node number, comparing the H factors of the leader nodes of the two teams, and taking the leader node with the high H factor as the leader node of all nodes of the two teams;
step 4): identifying and marking key members in the academic team by using a DHrank algorithm according to the academic team obtained in the step 3) to obtain important members in the academic team, including a leader and key members in the team;
4.1) calculating the degree centrality of all members in the team in the academic network, then calculating the sum of the degree centrality and the H factor of each student by combining the H factor calculated in the step 2), and measuring the academic influence of the students;
4.2) calculating the DHrank values of all members in the team in the following way:
wherein, DHR: (a i ) Show the scholarsa i DHRank value of (PR) ((R))a i ) For scholarsa i PageRank value of (c), Deg (c)a i ) Show the scholarsa i Degree in academic networks, hindex: (a i ) For scholarsa i H factor value of (d);
wherein, the scholars in the academic networka i The PageRank value is calculated as follows:
wherein E represents the set of all edges in the academic network; (a i ,a j ) Representing a student nodea i Anda j the connecting edge between the two edges is provided with a connecting edge,
representing a student nodea j Degree of (d);
let P be (PR)1,PR2,…PRn)TFor an n-dimensional PageRank vector, A is the corresponding transition matrix for the academic network, as follows:
wherein A: (A)a i ,a j ) Representing slave nodesa i To student nodea j The transition probability of (a) is,representing a student nodea i Degree of (d);
by adopting a poweriteration method, the iteration initial value of the PageRank value is designated as 1, the PageRank minimum value is set as 0 during iteration, and the solution is carried out through multiple iterations:
Pk＝ATPk-1
finally converging to Pk-Pk-1The | | is less than xi, namely the difference value of two iterations is less than the threshold xi; k represents the number of iterations at the end of the iteration;
after the DHRank value of each member in the team is obtained, the members in the team are arranged in a descending order according to the DHRank value, and the criticality of the members in the team is in direct proportion to the DHRank value.
2. The academic team important member identification method based on academic big data as claimed in claim 1, wherein the threshold value ξ ═ 10-5。
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