CN111309917A - Super-large scale academic network visualization method and system based on conference periodical galaxy diagram - Google Patents
Super-large scale academic network visualization method and system based on conference periodical galaxy diagram Download PDFInfo
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Abstract
The invention provides a super-large scale academic network visualization method and system based on a conference periodical galaxy diagram, comprising the following steps: step M1: acquiring relevant data of the thesis from a database, and storing the data in a file; step M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters; step M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information; step M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure; step M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result. The invention graphically displays the reference relationship among a large number of papers, so that the original abstract paper reference relationship becomes clearly visible.
Description
Technical Field
The invention relates to the technical field of data networks, in particular to a super-large scale academic network visualization method and system based on a conference journal star system diagram, and more particularly to a super-large scale academic network visualization method and system based on an academic conference or a journal star system diagram.
Background
With the rapid development of scientific technology, the number of academic papers is rapidly increasing, and this phenomenon directly leads to the rapid increase of the volume of academic networks. How to visualize a certain whole-field academic network becomes a problem which is urgently needed to be solved at present. However, the existing network layout algorithms such as Force Atlas, frontterman Reingold, Yifan Hu and the like cannot process large-scale academic network data. When the number of academic network nodes reaches about 10 thousands, the layout efficiency of the algorithm is sharply reduced, so that the algorithm can hardly output a layout result normally. The large-scale academic network data can be processed by the deep learning-based LargeVis algorithm, but accurate depiction of network details cannot be completed, only macroscopic features of the network can be shown, and after the graph is zoomed to a microscopic level, the graph is seriously overlapped, so that a user can hardly acquire any information amount from the graph.
The super-large scale network visualization algorithm needs to solve the problem of whether the layout can be performed after the data volume is increased sharply, and further needs to solve the problem of whether the local features are clearly and finely depicted after the layout is completed. None of the above algorithms satisfies these two requirements.
Patent document CN105718528A (application number: 201610029065.8) discloses an academic map display method based on citation relationship among papers, which comprises the following steps: step 1: clustering the thesis citation relation data acquired in advance by using a clustering algorithm and a distributed processing method, and dividing the data into a plurality of communities; step 2: analyzing the attributes and meanings of a plurality of communities, and storing related reference relation data into a database; and step 3: reading reference relation data in a database, constructing a thesis reference network, dynamically displaying the relation among the thesis by using a visualization tool, and finding out a target thesis; and 4, step 4: and displaying the reference relation among the papers on a plurality of visual angles to form an academic map.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a super-large scale academic network visualization method.
The invention provides a very large scale academic network visualization method based on a conference periodical galaxy diagram, which comprises the following steps:
step M1: acquiring relevant data of the thesis from a database, and storing the data in a file;
step M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters;
step M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information;
step M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
step M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result.
Preferably, the step M1 includes:
step M1.1: acquiring thesis related data including thesis ID data and thesis source data from a database, and storing the data in a file;
step M1.2: and acquiring reference relation data of the papers from the database according to the acquired relevant data of the papers, and storing the reference relation data in a file.
Preferably, the step M2 includes:
step M2.1: clustering according to published meetings or periodicals according to the relevant data of the papers stored in the documents;
step M2.2: extracting the reference relation between papers in each cluster;
step M2.3: using circles to represent nodes, using Bezier curves to represent continuous edges, and calculating related parameters of the nodes and the continuous edges according to the reference relation;
the calculation formula of the maximum node size is as follows:
wherein N represents the number of nodes within a single cluster;
calculation formula of each node size:
wherein InDegree represents the degree of clustering; InDegreemaxRepresenting the maximum in-degree in the cluster;
the calculation formula of the edge connecting RGB value is as follows:
wherein R ise,Ge,BeRepresenting the RGB values of the connected edges; r1,G1,B1An RGB value representing node 1; r2,G2,B2An RGB value representing node 2;
step M2.4: and storing the nodes and the side information of the corresponding clusters obtained by data clustering and processing in a graph file.
Preferably, the step M3 includes:
step M3.1: setting a configuration file of the automatic layout tool to determine a specific workflow of the automatic layout tool;
step M3.2: and rapidly arranging clustering results according to published meetings or periodicals by using an automatic arrangement tool, and storing the clustering results according to a picture file with a preset format.
Preferably, the step M4 includes:
step M4.1: according to the thesis reference relationship among the clusters, the gravity of different clusters under the force guide model is equivalent;
step M4.2: according to the citation relation of the paper in the cluster, the size of the repulsive force of different clusters under the force guidance model is equivalent;
step M4.3: calculating the size of the clustering equivalent nodes according to the internal layout result of the clusters, and writing the size of the clustering equivalent nodes into a graph file containing the size of the clustering equivalent nodes, wherein the formula is as follows:
wherein r represents the distance of the large coordinate origin of each node; (x, y) represents the position coordinates each node contains;
wherein r iseRepresenting the size of the clustering equivalent nodes; r ismaxRepresenting that each node in the graph file containing the size of the cluster equivalent node is traversed to obtain the distance corresponding to the node farthest from the origin of coordinates in the current cluster, β representing that the sizes of all equivalent nodes are adjusted in equal proportion;
step M4.4: obtaining a star system structure among clusters by using a ForceAttlas 2 algorithm according to the sizes of the attraction force and the repulsion force of different clusters under the force guidance model and the cluster equivalent nodes, and storing the star system structure according to a preset format;
the force guide model refers to a type of layout algorithm for laying out the graph;
the step M5 includes:
step M5.1: according to the obtained star system structure and the internal layout result of the cluster, fusing the graph file obtained in the step M2 with the graph file obtained in the step M3 and the star system structure, and storing the fused graph file in a preset format;
step M5.2: and converting the fused image file into a bitmap file by using python.
The invention provides a very large scale academic network visualization system based on a conference periodical galaxy diagram, which comprises:
module M1: acquiring relevant data of the thesis from a database, and storing the data in a file;
module M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters;
module M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information;
module M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
module M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result.
Preferably, said module M1 comprises:
module M1.1: acquiring thesis related data including thesis ID data and thesis source data from a database, and storing the data in a file;
module M1.2: and acquiring reference relation data of the papers from the database according to the acquired relevant data of the papers, and storing the reference relation data in a file.
Preferably, said module M2 comprises:
module M2.1: clustering according to published meetings or periodicals according to the relevant data of the papers stored in the documents;
module M2.2: extracting the reference relation between papers in each cluster;
module M2.3: using circles to represent nodes, using Bezier curves to represent continuous edges, and calculating related parameters of the nodes and the continuous edges according to the reference relation;
the calculation formula of the maximum node size is as follows:
wherein N represents the number of nodes within a single cluster;
calculation formula of each node size:
wherein InDegree represents the degree of clustering; InDegreemaxRepresenting the maximum in-degree in the cluster;
the calculation formula of the edge connecting RGB value is as follows:
wherein R ise,Ge,BeRepresenting the RGB values of the connected edges; r1,G1,B1An RGB value representing node 1; r2,G2,B2An RGB value representing node 2;
module M2.4: and storing the nodes and the side information of the corresponding clusters obtained by data clustering and processing in a graph file.
Preferably, said module M3 comprises:
module M3.1: setting a configuration file of the automatic layout tool to determine a specific workflow of the automatic layout tool;
module M3.2: and rapidly arranging clustering results according to published meetings or periodicals by using an automatic arrangement tool, and storing the clustering results according to a picture file with a preset format.
Preferably, said module M4 comprises:
module M4.1: according to the thesis reference relationship among the clusters, the gravity of different clusters under the force guide model is equivalent;
module M4.2: according to the citation relation of the paper in the cluster, the size of the repulsive force of different clusters under the force guidance model is equivalent;
module M4.3: calculating the size of the clustering equivalent nodes according to the internal layout result of the clusters, and writing the size of the clustering equivalent nodes into a graph file containing the size of the clustering equivalent nodes, wherein the formula is as follows:
wherein r represents the distance of the large coordinate origin of each node; (x, y) represents the position coordinates each node contains;
wherein r iseRepresenting the size of the clustering equivalent nodes; r ismaxRepresenting that each node in the graph file containing the size of the cluster equivalent node is traversed to obtain the distance corresponding to the node farthest from the origin of coordinates in the current cluster, β representing that the sizes of all equivalent nodes are adjusted in equal proportion;
module M4.4: obtaining a star system structure among clusters by using a ForceAttlas 2 algorithm according to the sizes of the attraction force and the repulsion force of different clusters under the force guidance model and the cluster equivalent nodes, and storing the star system structure according to a preset format;
the force guide model refers to a type of layout algorithm for laying out the graph;
the module M5 includes:
module M5.1: according to the obtained star system structure and the internal layout result of the cluster, fusing the graph file obtained by the module M2 with the graph file obtained by the module M3 with the star system structure, and storing the fused graph file in a preset format;
module M5.2: and converting the fused image file into a bitmap file by using python.
Compared with the prior art, the invention has the following beneficial effects:
1. the super-large scale academic network is visualized based on the academic conference or the periodical astrology diagram, the relative relation of the conference or the periodical in the academic network in the whole field can be clearly and effectively displayed, and the barrier of network visualization algorithm in the order of four million is broken through; the network details can be clearly and accurately visualized;
2. according to the invention, clustering is carried out from the perspective of a meeting or a journal published by a paper, and an automatic drawing tool is used for carrying out rapid layout, so that the overall appearance of the whole academic network is disclosed macroscopically, and the distribution condition of the papers in the cluster can be clearly shown;
3. the invention is not only used for revealing the whole appearance of the academic map in the whole field of the computer, but also can be expanded to the revealing of the academic whole appearance in other fields;
4. the invention graphically displays the reference relationship among a large number of papers, so that the original abstract paper reference relationship becomes clearly visible.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 shows the internal layout effects of AAAI, ICCV, INFOCOM, and MOBICOM clusters, nodes representing papers published at these meetings;
FIG. 3 is the resulting inter-cluster galaxy structure;
FIG. 4 is a macroscopic overview of the visualization, i.e., the distribution of computer domain meetings or periodicals;
FIG. 5 is a partial detail of a visualization, a distribution of meetings or periodicals associated with computer vision;
FIG. 6 is a partial detail of a visualization, a distribution of meetings or periodicals associated with a computer network;
fig. 7 is a detail of a part of the enlarged visualization result, which is the distribution of nodes inside the top-level periodical TIT cluster in the information theory direction.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention provides a very large scale academic network visualization method based on a conference periodical galaxy diagram, which comprises the following steps:
step M1: acquiring relevant data of the thesis from a database, and storing the data in a file;
specifically, the step M1 includes:
step M1.1: acquiring thesis related data including thesis ID data and thesis source data from a database, and storing the data in a file;
step M1.2: and acquiring reference relation data of the papers from the database according to the acquired relevant data of the papers, and storing the reference relation data in a file.
Step M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters;
specifically, the step M2 includes:
step M2.1: clustering according to published meetings or periodicals according to the relevant data of the papers stored in the documents;
step M2.2: extracting the reference relation between papers in each cluster;
step M2.3: using circles to represent nodes, using Bezier curves to represent continuous edges, and calculating related parameters of the nodes and the continuous edges according to the reference relation;
the calculation formula of the maximum node size is as follows:
wherein N represents the number of nodes within a single cluster;
calculation formula of each node size:
wherein InDegree represents the degree of clustering; InDegreemaxRepresenting the maximum in-degree in the cluster;
the calculation formula of the edge connecting RGB value is as follows:
wherein R ise,Ge,BeRepresenting the RGB values of the connected edges; r1,G1,B1An RGB value representing node 1; r2,G2,B2An RGB value representing node 2;
step M2.4: and storing the nodes and the side information of the corresponding clusters obtained by data clustering and processing in a graph file.
Step M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information;
specifically, the step M3 includes:
step M3.1: setting a configuration file of the automatic layout tool to determine a specific workflow of the automatic layout tool;
step M3.2: and rapidly arranging clustering results according to published meetings or periodicals by using an automatic arrangement tool, and storing the clustering results according to a picture file with a preset format.
Step M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
specifically, the step M4 includes:
step M4.1: according to the thesis reference relationship among the clusters, the gravity of different clusters under the force guide model is equivalent;
step M4.2: according to the citation relation of the paper in the cluster, the size of the repulsive force of different clusters under the force guidance model is equivalent;
step M4.3: calculating the size of the clustering equivalent nodes according to the internal layout result of the clusters, and writing the size of the clustering equivalent nodes into a graph file containing the size of the clustering equivalent nodes, wherein the formula is as follows:
wherein r represents the distance of the large coordinate origin of each node; (x, y) represents the position coordinates each node contains;
wherein r iseRepresenting the size of the clustering equivalent nodes; r ismaxRepresenting that each node in the graph file containing the size of the cluster equivalent node is traversed to obtain the distance corresponding to the node farthest from the origin of coordinates in the current cluster, β representing that the sizes of all equivalent nodes are adjusted in equal proportion;
step M4.4: obtaining a star system structure among clusters by using a ForceAttlas 2 algorithm according to the sizes of the attraction force and the repulsion force of different clusters under the force guidance model and the cluster equivalent nodes, and storing the star system structure according to a preset format;
the force guide model refers to a type of layout algorithm for laying out the graph;
step M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result.
The step M5 includes:
step M5.1: according to the obtained star system structure and the internal layout result of the cluster, fusing the graph file obtained in the step M2 with the graph file obtained in the step M3 and the star system structure, and storing the fused graph file in a preset format;
step M5.2: and converting the fused image file into a bitmap file by using python.
The invention provides a very large scale academic network visualization system based on a conference periodical galaxy diagram, which comprises:
module M1: acquiring relevant data of the thesis from a database, and storing the data in a file;
specifically, the module M1 includes:
module M1.1: acquiring thesis related data including thesis ID data and thesis source data from a database, and storing the data in a file;
module M1.2: and acquiring reference relation data of the papers from the database according to the acquired relevant data of the papers, and storing the reference relation data in a file.
Module M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters;
specifically, the module M2 includes:
module M2.1: clustering according to published meetings or periodicals according to the relevant data of the papers stored in the documents;
module M2.2: extracting the reference relation between papers in each cluster;
module M2.3: using circles to represent nodes, using Bezier curves to represent continuous edges, and calculating related parameters of the nodes and the continuous edges according to the reference relation;
the calculation formula of the maximum node size is as follows:
wherein N represents the number of nodes within a single cluster;
calculation formula of each node size:
wherein InDegree represents the degree of clustering; InDegreemaxRepresenting the maximum in-degree in the cluster;
the calculation formula of the edge connecting RGB value is as follows:
wherein R ise,Ge,BeRepresenting the RGB values of the connected edges; r1,G1,B1An RGB value representing node 1; r2,G2,B2An RGB value representing node 2;
module M2.4: and storing the nodes and the side information of the corresponding clusters obtained by data clustering and processing in a graph file.
Module M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information;
specifically, the module M3 includes:
module M3.1: setting a configuration file of the automatic layout tool to determine a specific workflow of the automatic layout tool;
module M3.2: and rapidly arranging clustering results according to published meetings or periodicals by using an automatic arrangement tool, and storing the clustering results according to a picture file with a preset format.
Module M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
specifically, the module M4 includes:
module M4.1: according to the thesis reference relationship among the clusters, the gravity of different clusters under the force guide model is equivalent;
module M4.2: according to the citation relation of the paper in the cluster, the size of the repulsive force of different clusters under the force guidance model is equivalent;
module M4.3: calculating the size of the clustering equivalent nodes according to the internal layout result of the clusters, and writing the size of the clustering equivalent nodes into a graph file containing the size of the clustering equivalent nodes, wherein the formula is as follows:
wherein r represents the distance of the large coordinate origin of each node; (x, y) represents the position coordinates each node contains;
wherein r iseRepresenting the size of the clustering equivalent nodes; r ismaxRepresenting traversal of a graph file containing cluster equivalent node sizesβ represents that the equivalent nodes are adjusted in equal proportion;
module M4.4: obtaining a star system structure among clusters by using a ForceAttlas 2 algorithm according to the sizes of the attraction force and the repulsion force of different clusters under the force guidance model and the cluster equivalent nodes, and storing the star system structure according to a preset format;
the force guide model refers to a type of layout algorithm for laying out the graph;
module M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result.
The module M5 includes:
module M5.1: according to the obtained star system structure and the internal layout result of the cluster, fusing the graph file obtained by the module M2 with the graph file obtained by the module M3 with the star system structure, and storing the fused graph file in a preset format;
module M5.2: and converting the fused image file into a bitmap file by using python.
The present invention is further described in detail by the following preferred examples:
taking 4328431 papers in the whole field of computers and a super-large scale academic network formed by the citation relations thereof as an example, the super-large scale academic network visualization method based on the academic conference or the journal astrology diagram provided by the embodiment relates to the arrangement of the super-large scale academic network visualization method comprising the steps of obtaining the paper ID and the attribute from a database, clustering the papers based on the thesis source, rapidly arranging the classification result by using an automatic layout tool, and generating the fusion of the inter-cluster astrology structure and the diagram based on the inter-cluster citation relation and the force guidance model; specifically, as shown in fig. 1, the method comprises the following steps:
step S1: acquiring data including a thesis ID, a thesis source, a thesis reference relation and the like from a database, and storing the data into a file;
step S2: clustering the papers according to the paper sources and the meetings or periodicals of the papers, and generating corresponding classified picture files;
step S3: rapidly arranging the classification results by using an automatic arrangement tool to obtain the internal arrangement of the clusters;
step S4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
step S5: and fusing clusters according to the inter-cluster star system structure and the intra-cluster layout to obtain a visual result.
Step S1 includes: and acquiring data including a paper ID, a paper source, a paper reference relation and the like from a database, and storing the data into a file. The Acemap database is a relational database based on MySQL, and stores massive academic information related to papers, scholars, academic institutions, academic conferences or periodicals and the like. Specifically, the method comprises the following steps:
step S101: the ID, title, and conference or journal published in 4328431 papers in the computer world are obtained from the aceap database. After the relevant information of the paper is acquired, the paper information is stored in a JSON file in the following format for later use:
[{“paper_id”:paper_id0,“title”:title0,“paper_source”:paper_source0},{“paper_id”:paper_id1,“title”:title1,“paper_source”:paper_source1},…]
where "paper _ ID" is used to index the paper ID, "title" is used to index the paper title, and "paper _ source" is used to index the meeting or journal published by the paper.
Step S102: acquiring reference relation data of the papers from an Acemap database according to the following steps:
1. a set of paper IDs is created for storing the IDs of all papers, no duplicate paper IDs being present in this set.
2. And traversing the set paper _ IDs, querying the database, finding each paper ID and the IDs of all papers quoted by the paper, and ensuring that the paper IDs are all elements in the set paper _ IDs, thereby ensuring that the two ends of the quote relationship are all elements in the set paper _ IDs.
3. Storing the obtained reference relation in a JSON file according to the following format:
[ { "source": source _ paper _ ID0, "target": target _ paper _ ID0}, { "source": source _ paper _ ID1, "target": target _ paper _ ID1}, … ] wherein "source" is used to index the original paper ID and "target" is used to index the ID of the referenced paper, i.e., the "source" paper references the "target" paper.
Step S2 includes: clustering the papers according to meetings or periodicals according to the paper source of the papers, wherein 4808 meetings and 1629 periodicals are generated, and graph files containing nodes and connecting edge attributes are correspondingly classified according to layout requirements. The thesis clustering is a key step in the super-large scale academic network visualization, and the result of clustering directly determines the macroscopic effect of the visualization result. We need to make the number of elements in the cluster appropriate for post-processing. Besides, the meaning represented by the clustering result should be easy to understand. Specifically, the method comprises the following steps:
step S201: to obtain a better clustering effect, a journal or a conference published by a paper is selected as a clustering standard. The papers published in the same meeting or periodical are divided into the same cluster by traversing the paper information list, and the cluster ID is named by the abbreviated name of the meeting or periodical. And when the traversal is completed, obtaining the clustering information of all the papers.
Step S202: according to the cluster information obtained in step S201, the thesis information data obtained in step S101, and the reference relationship data obtained in step S102, the reference relationship inside each cluster is extracted according to the following steps:
1. according to the clustering information obtained in the step S201, traversing the thesis information data obtained in the step S101 to obtain a mapping from the thesis ID to the clustering ID, and storing the mapping in the following format:
{“paper_id0”:cluster_id0,“paper_id1”:cluster_id1,…}
where "paper _ ID" represents the ID of the paper and cluster _ ID represents the cluster ID to which the paper belongs.
2. And traversing the reference relation data obtained in the step S102, and judging whether the cluster ID corresponding to the ID of the source paper is the same as the cluster ID corresponding to the ID of the target paper by using the mapping obtained in the step 1. If the two are the same, a reference relationship inside the cluster is obtained. And when the traversal is completed, obtaining the reference relations in all the clusters.
Step S203: the clustered internal papers are represented by using circles on a 2D plane as nodes, wherein the size represents the quoted quantity of the papers in the network, and the same color represents that the nodes have stronger correlation; the citation relationship between the papers is expressed by using a Bezier curve as a continuous edge, the color is a mixture of colors of two nodes, and the thickness of the edge is constant and is 1 pixel.
The specific relevant parameters are calculated as follows:
1. calculating the node size:
assuming that the number of nodes in a single cluster is N, the introductions of the nodes can be used to represent the quoted amount of the paper in the cluster, and assuming that the maximum introductions in the cluster is InDegreeemaxWe can empirically obtain the formula for calculating the maximum size of the node in this example:
and further obtaining a calculation formula of the size of each node:
2. dividing node colors:
and arranging the nodes in a descending order according to the degree of income to obtain an ordered set of the nodes, taking the first node in the set, namely the node with the maximum degree of income in the current set, and then finding all the nodes which refer to the thesis. And the node with the highest current in-degree is assigned the same color as the node which refers to the changed paper to represent the relevance of the reference relation. These nodes are then removed from the collection and the above operations are repeated until the collection is empty, i.e., the coloring of all nodes is completed.
3. Calculation of edge color:
the colors of the nodes and edges are characterized by three parameter values of RGB, assuming the RGB values of node 1 and node 2Is other than R1,G1,B1And R2,G2,B2Then the RGB values of the edges are calculated by:
step S204: according to the reference relationship between the paper clustering information obtained in step S201 and the cluster interior obtained in step S202 and the relevant parameters of the nodes and the edges obtained in step S203, a graph file is generated by using the paper in each cluster as a node, the reference relationship as an edge and the cluster ID as a file name, and is stored in the format of a gml file:
the meaning represented by each field is illustrated by the "#" sign of that field followed by a suffix.
Step S3 includes: and (3) rapidly laying out the classification result by using gephi-tool to obtain the internal layout of the cluster, and generating a graph file containing node position information, wherein the internal layout effect of part of the cluster is shown in figure 2. The gephi-tool is a java-based command line automatic rapid layout tool developed by an inemap team, and has the greatest characteristic of being capable of rapidly and parallelly laying out a large number of image files. Specifically, the method comprises the following steps:
step S301: setting a config file of the gephi-tool, determining an algorithm required by the layout and specific parameters in the algorithm, wherein the specific parameters of the config file are as follows:
the "label _ font _ size _ ratio" field is used to set the scaling of the characters and the node size in the graph, and the "layout" field is a list representing the layout algorithm used in the graph layout process. The specific "algorithm" represents the name of the layout algorithm, the "args" field represents parameters required by the layout algorithm, and the "iteration" field represents the number of iterations of the algorithm. It should be noted that the gephi-tool runs according to the order and the number of iterations of the algorithm in the "layout" list.
Step S302: according to the config file of step S301 and the graph file of the corresponding cluster obtained in step S2, layout is performed using gephi-tool, and the graph file including the location information after layout is completed is stored in the format of the gml file as follows:
compared with the gml file obtained in step S204, after the layout, the nodes have added coordinate attributes. The location attribute in the gml file is automatically added by gephi-tool.
Step S4 includes: according to the reference relationship between the interior of the cluster and the cluster, the force between the equivalent clusters, and the internal layout of the cluster obtained in step S3, the force-guided algorithm ForceAtlas2 is used to generate the inter-cluster star system structure as shown in fig. 3, specifically:
step S401: and equating a connecting edge between clusters with reference relationship, equating the weight of the connecting edge according to the reference relationship of the thesis between the clusters, and controlling the gravity of different clusters under the force guide model by equating the weight of different connecting edges.
Suppose that there is a reference relationship between cluster A and cluster B, and the number of edges of the articles in cluster A that refer to the articles in cluster B is RefAThe number of edges of the papers in cluster A quoted by the papers in cluster B is RefBThe total number of edges of the cluster A referring to other clusters is RefSUMAThe total number of edges connecting the cluster B to other clusters is RefSUMBThen, the weight of the equivalent connecting edge between the cluster a and the cluster B is determined by the following formula:
of these α, α in this example was 1000, to balance the effect of repulsion.
Calculating the weight of equivalent connecting edges between any clusters with reference relations according to the formula, and writing all the edges and the corresponding weights into a structure.gml file according to the following format for subsequent use:
wherein, the source field represents the ID of the source cluster, the target field represents the ID of the target cluster, and the weight field is the w in the formulaA,B。
According to the formula of gravity in ForceAtlas2, the equivalent gravity between clusters a and B is obtained as:
Fa(A,B)＝wA,B·dA,B
wherein, wA,BFor the weights of the equivalent edges obtained above, dA,BIs the distance between cluster a and the equivalent center of cluster B.
Step S402: when the repulsion between clusters is calculated, the algorithm only concerns the degree of clustering, and the degree inside the clusters is generated by the connecting edges inside the clusters. Therefore, according to the reference relation of the papers in the clusters, the clusters are equivalent, simple clusters with equivalent degrees are obtained, and the size of the repulsive force among different clusters is controlled.
Assume that the number of Edge connecting inside cluster A is EdgeAThen the degree inside cluster A is 2. EdgeAThen the degree inside cluster a is:
similarly, the degree inside cluster B is:
all communities are equivalent to circular nodes with unequal sizes (how to determine the size of equivalent nodes is introduced in step S403) And carrying out equivalence on the degree inside the cluster by adding an auxiliary node at the periphery of the equivalent node and a method for connecting the auxiliary node with the equivalent node. For the cluster A, the number of the attached nodes and the number of the connecting edges added by the equivalent nodes are both
(int () is a rounding operation) where k is used to adjust the number of nodes that increase in equivalence, k taking 100 in this example. Writing the equivalent nodes and the auxiliary nodes of all the clusters into a structure.
Equivalent nodes:
wherein, the ID field represents the ID of the cluster, w, h and d represent the size of the node, when the shape of the node is a circle, the three parameters are equal, the fill field represents the color of the node, and the node is randomly distributed during writing.
Subsidiary nodes of the equivalent nodes:
wherein, the id field represents the first subsidiary node of the cluster A, the w, the h and the d represent the size of the node, when the shape of the node is a circle, the three parameters are equal, and the fill field represents the color of the node.
Writing the connecting edges of the equivalent nodes and the auxiliary nodes thereof into the following format:
the source field represents an ID of an attached node of an equivalent node, and the target field represents an ID of the equivalent node, it should be noted that, in order to "tightly" bind the attached node around the equivalent node, a value of the weight field needs to be much larger than an equivalent weight value between any two clusters.
Then the equivalent repulsion between clusters a and B is found according to the repulsion formula in ForceAtlas2 as:
dA,Bis the distance between the equivalent centers of gravity of cluster a and cluster B.
Step S403: calculating the size of the cluster equivalent node according to the internal layout of the cluster obtained in the step S3, and writing the size into a structure.
After the layout of the graph file in step S3, the graph file already contains the location information, each node contains the location coordinates (x, y), and the distance from each node to the coordinate origin is:
traversing each node in the graph to obtain the distance r corresponding to the node farthest from the origin of coordinates in the clustermaxThen, the size of the clustering equivalent node is:
wherein β is used to scale the size of all equivalent nodes, in this case β takes 100.
And writing the sizes of all the clustering equivalent nodes into the fields of w, h and d of the corresponding clustering equivalent nodes in the structure.
Step S404: and (3) using a Gephi GUI tool to layout the structure. gml file to obtain an inter-cluster star system structure:
as the star system structure among clusters directly influences the macroscopic effect of the final visual result, Gephi's GUI tool is used for layout. Construct.gml file is first imported into Gephi's GUI tool. Because the size of the equivalent node represents the range size of the corresponding cluster, in order to prevent the clusters from overlapping and further to make the layout result clearer, the equivalent nodes need to be ensured not to overlap with each other.
To achieve this, the ForceAtlas2 algorithm is required to lay out in overlap elimination mode. However, the layout effect is seriously affected by opening the overlap removal mode too early, so that the non-overlap removal mode of the layout algorithm needs to be used first, and the overlap removal mode is opened after the layout is stable. When the algorithm is finally stabilized, the inter-cluster star system structure can be obtained as shown in fig. 3.
Exporting the star system structure into a json file format, naming the json as structure, and storing the json file format according to the following format:
{“node_id_0”:{“x”:x,“y”:y,“size”:size},“node_id_1”:{“x”:x,“y”:y,“size”:size}…}
wherein, the "node _ ID" field represents the ID of the equivalent node of the cluster, i.e. the ID of the cluster, the "x" and "y" fields represent the coordinates of the equivalent node, and the "size" field represents the size of the equivalent node.
Step S5 includes: and fusing clusters according to the inter-cluster star system structure and the intra-cluster layout to obtain a visual result:
in step S403, the real size of the cluster is reduced by β times, so that the coordinates of all cluster equivalent nodes in structure json need to be multiplied by β, and the obtained equivalent node coordinates are the center coordinates of the real cluster.
Step S502: and fusing graphs according to the cluster center coordinates obtained in the step S501 and the cluster internal layout obtained in the step S3:
suppose a cluster has a central node of (x)0,y0) And the coordinates of each node after the clustering layout is finished are [. x [ ] x1,y1),(x2,y2),(x3,y3),...,(xk,yk)]Then, the position of each node in the cluster in the fused graph is:
[(x1+x0,y1+y0),(x2+x0,y2+y0),...,(xk+x0,yk+y0)]
step S503: traversing all the clustered graph files obtained in the step S3, writing the IDs, sizes, colors, titles, connecting edges inside the clusters, and the position information of each node obtained in the step S502 into the graph files, and storing the gml file according to the following format:
step S504: the graph file containing all the node and edge related attribute values of the full graph obtained in step S503 is converted into a bitmap file by using python, that is, a visualization result is obtained, as shown in fig. 5 to fig. 7.
Firstly, the relative relation of conferences or periodicals in the academic network in the whole field is clearly and effectively displayed, and the barrier of network visualization algorithm in the order of four million is broken through. Secondly, clustering is carried out from the perspective of a conference or a periodical published by papers, and an automatic drawing tool is used for carrying out rapid layout, so that the overall appearance of the whole academic network is macroscopically disclosed, and the distribution condition of papers in the cluster can be clearly shown. Thirdly, the invention is not only used for revealing the full view of the computer full-field academic map, but also can be extended to the revealing of the full view of other fields. Finally, the invention graphically displays the reference relationship among a large number of papers, so that the original abstract paper reference relationship becomes clearly visible.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A very large scale academic network visualization method based on a conference periodical galaxy diagram is characterized by comprising the following steps:
step M1: acquiring relevant data of the thesis from a database, and storing the data in a file;
step M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters;
step M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information;
step M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
step M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result.
2. The method for visualizing the very large scale academic network based on the conference journal galaxy map as claimed in claim 1, wherein the step M1 comprises:
step M1.1: acquiring thesis related data including thesis ID data and thesis source data from a database, and storing the data in a file;
step M1.2: and acquiring reference relation data of the papers from the database according to the acquired relevant data of the papers, and storing the reference relation data in a file.
3. The method for visualizing the very large scale academic network based on the conference journal galaxy map as claimed in claim 1, wherein the step M2 comprises:
step M2.1: clustering according to published meetings or periodicals according to the relevant data of the papers stored in the documents;
step M2.2: extracting the reference relation between papers in each cluster;
step M2.3: using circles to represent nodes, using Bezier curves to represent continuous edges, and calculating related parameters of the nodes and the continuous edges according to the reference relation;
the calculation formula of the maximum node size is as follows:
wherein N represents the number of nodes within a single cluster;
calculation formula of each node size:
wherein InDegree represents the degree of clustering; InDegreemaxRepresenting the maximum in-degree in the cluster;
the calculation formula of the edge connecting RGB value is as follows:
wherein R ise,Ge,BeRepresenting the RGB values of the connected edges; r1,G1,B1An RGB value representing node 1; r2,G2,B2An RGB value representing node 2;
step M2.4: and storing the nodes and the side information of the corresponding clusters obtained by data clustering and processing in a graph file.
4. The method for visualizing the very large scale academic network based on the conference journal galaxy map as claimed in claim 1, wherein the step M3 comprises:
step M3.1: setting a configuration file of the automatic layout tool to determine a specific workflow of the automatic layout tool;
step M3.2: and rapidly arranging clustering results according to published meetings or periodicals by using an automatic arrangement tool, and storing the clustering results according to a picture file with a preset format.
5. The method for visualizing the very large scale academic network based on the conference journal galaxy map as claimed in claim 1, wherein the step M4 comprises:
step M4.1: according to the thesis reference relationship among the clusters, the gravity of different clusters under the force guide model is equivalent;
step M4.2: according to the citation relation of the paper in the cluster, the size of the repulsive force of different clusters under the force guidance model is equivalent;
step M4.3: calculating the size of the clustering equivalent nodes according to the internal layout result of the clusters, and writing the size of the clustering equivalent nodes into a graph file containing the size of the clustering equivalent nodes, wherein the formula is as follows:
wherein r represents the distance of the large coordinate origin of each node; (x, y) represents the position coordinates each node contains;
wherein r iseRepresenting the size of the clustering equivalent nodes; r ismaxRepresenting and traversing each node in the graph file containing the cluster equivalent node size to obtain the distance corresponding to the node farthest from the origin of coordinates in the current clusterβ shows the size of all equivalent nodes is adjusted in equal proportion;
step M4.4: obtaining a star system structure among clusters by using a ForceAttlas 2 algorithm according to the sizes of the attraction force and the repulsion force of different clusters under the force guidance model and the cluster equivalent nodes, and storing the star system structure according to a preset format;
the force guide model refers to a type of layout algorithm for laying out the graph;
the step M5 includes:
step M5.1: according to the obtained star system structure and the internal layout result of the cluster, fusing the graph file obtained in the step M2 with the graph file obtained in the step M3 and the star system structure, and storing the fused graph file in a preset format;
step M5.2: and converting the fused image file into a bitmap file by using python.
6. A very large scale academic network visualization system based on a conference periodical galaxy diagram is characterized by comprising:
module M1: acquiring relevant data of the thesis from a database, and storing the data in a file;
module M2: clustering the papers according to meetings or periodicals according to the related data of the papers, and generating a graph file containing nodes and connecting edge parameters of corresponding clusters;
module M3: rapidly laying out the graph files of the nodes and the connecting edge parameters of the corresponding clusters by using an automatic layout tool to obtain the internal layout of the clusters and generate the graph files containing the node position information;
module M4: according to the reference relationship among the clusters, the force between the clusters is equivalent, and a force guide algorithm is used for generating an inter-cluster star system structure;
module M5: and fusing the clusters according to the inter-cluster star system structure to obtain a visual result.
7. The system for visualizing a very large scale academic network based on a conference journal galaxy as claimed in claim 6, wherein the module M1 comprises:
module M1.1: acquiring thesis related data including thesis ID data and thesis source data from a database, and storing the data in a file;
module M1.2: and acquiring reference relation data of the papers from the database according to the acquired relevant data of the papers, and storing the reference relation data in a file.
8. The system for visualizing a very large scale academic network based on a conference journal galaxy as claimed in claim 6, wherein the module M2 comprises:
module M2.1: clustering according to published meetings or periodicals according to the relevant data of the papers stored in the documents;
module M2.2: extracting the reference relation between papers in each cluster;
module M2.3: using circles to represent nodes, using Bezier curves to represent continuous edges, and calculating related parameters of the nodes and the continuous edges according to the reference relation;
the calculation formula of the maximum node size is as follows:
wherein N represents the number of nodes within a single cluster;
calculation formula of each node size:
wherein InDegree represents the degree of clustering; InDegreemaxRepresenting the maximum in-degree in the cluster;
the calculation formula of the edge connecting RGB value is as follows:
wherein R ise,Ge,BeRepresenting the RGB values of the connected edges; r1,G1,B1An RGB value representing node 1; r2,G2,B2An RGB value representing node 2;
module M2.4: and storing the nodes and the side information of the corresponding clusters obtained by data clustering and processing in a graph file.
9. The system for visualizing a very large scale academic network based on a conference journal galaxy as claimed in claim 6, wherein the module M3 comprises:
module M3.1: setting a configuration file of the automatic layout tool to determine a specific workflow of the automatic layout tool;
module M3.2: and rapidly arranging clustering results according to published meetings or periodicals by using an automatic arrangement tool, and storing the clustering results according to a picture file with a preset format.
10. The system for visualizing a very large scale academic network based on a conference journal galaxy as claimed in claim 6, wherein the module M4 comprises:
module M4.1: according to the thesis reference relationship among the clusters, the gravity of different clusters under the force guide model is equivalent;
module M4.2: according to the citation relation of the paper in the cluster, the size of the repulsive force of different clusters under the force guidance model is equivalent;
module M4.3: calculating the size of the clustering equivalent nodes according to the internal layout result of the clusters, and writing the size of the clustering equivalent nodes into a graph file containing the size of the clustering equivalent nodes, wherein the formula is as follows:
wherein r represents the distance of the large coordinate origin of each node; (x, y) represents the position coordinates each node contains;
wherein r iseRepresenting the size of the clustering equivalent nodes; r ismaxRepresenting that each node in the graph file containing the size of the cluster equivalent node is traversed to obtain the distance corresponding to the node farthest from the origin of coordinates in the current cluster, β representing that the sizes of all equivalent nodes are adjusted in equal proportion;
module M4.4: obtaining a star system structure among clusters by using a ForceAttlas 2 algorithm according to the sizes of the attraction force and the repulsion force of different clusters under the force guidance model and the cluster equivalent nodes, and storing the star system structure according to a preset format;
the force guide model refers to a type of layout algorithm for laying out the graph;
the module M5 includes:
module M5.1: according to the obtained star system structure and the internal layout result of the cluster, fusing the graph file obtained by the module M2 with the graph file obtained by the module M3 with the star system structure, and storing the fused graph file in a preset format;
module M5.2: and converting the fused image file into a bitmap file by using python.
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