CN112199437A - Academic network visual presentation method and system based on jump between star cloud pictures - Google Patents
Academic network visual presentation method and system based on jump between star cloud pictures Download PDFInfo
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Abstract
The invention provides an academic network visual presentation method and system based on jump between star cloud pictures, comprising the following steps: step M1: acquiring nodes and connecting edges of an academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers; step M2: setting the color and the size of the node and the color and the size of the connecting edge according to the node and the connecting edge of the academic network, and storing the node, the connecting edge and corresponding attributes in a graph file; step M3: the graph file is used for laying out an academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file; step M4: storing the content in the graph file in a database; step M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the academic network star cloud picture by using a mode of inter-picture jumping. The method can quickly complete visual presentation of the mass academic networks led by the high-lead papers in the whole field, so that the knowledge structure is visual.
Description
Technical Field
The invention relates to the technical field of data networks, in particular to a method and a system for visually presenting a mass academic network, and more particularly to a method and a system for visually presenting an academic network based on jump between star-cloud pictures.
Background
With the continuous progress of scientific technology, the number of papers as knowledge carriers is rapidly increasing, and in the process, numerous academic papers with influence are born. A high impact paper is often cited by a large number of papers. The high-influence paper and the paper which quotes the article are regarded as nodes in the network, and the quoted relation between the nodes is regarded as a connecting edge in the network, so that the academic network guided by the high-quotation seal can be obtained. If we lay out the network using a force directed algorithm such as ForceAtlas2, and set the color sizes of the nodes and edges, etc., we get a star cloud map of the academic network. However, in the context of big data, high-index papers still exist in large numbers, and if the statistics of the quoted amount is larger than 1000, the number of the currently known high-index papers exceeds 47310, that is, there are academic networks leading to more than 47310 high-index papers. At present, a method for visualizing academic networks, such as visualizing a single network by using a Gephi GUI tool, usually needs a large amount of manual intervention, is relatively complex to operate, and obviously cannot meet the requirement for visualizing a large amount of academic networks. In order to perform visual presentation and visual perception on a knowledge structure of an academic big data space and perform interaction, a method and a visual presentation system capable of quickly visualizing a massive academic network are needed.
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. When the patent is used for processing an academic network with a large scale, the front-end direct rendering real-time performance is not strong, so that the experience degree of a user is reduced, and the capability of rapid visual presentation of a large amount of academic networks is not provided.
Patent document CN105808729B (application number: 201610131343.0) discloses an academic big data analysis method based on citation relationship among papers, which comprises the steps of 1: correspondingly analyzing and processing a local thesis data set, and then constructing a thesis citation network in a database; step 2: constructing an analysis algorithm according to the citation relationship in the thesis citation network, obtaining the importance and the relationship of the nodes in the thesis citation network through the analysis algorithm, and obtaining the importance of the thesis relative to a central thesis; and step 3: and (3) converting the one-to-one citation relation of the papers into a mapping set of the citation direction and a mapping set of the cited direction, acquiring development paths among the appointed papers in the paper citation network through an extraction algorithm, and calculating the importance of the paths according to the importance of the papers acquired in the step 2.
Patent document CN106844665A (application number: 201710051673.3) discloses a paper recommendation method based on reference relationship distributed expression. The context of the papers in the weight reference network is expressed by using the distributed vector, and then the similarity between the papers is calculated by using the vector, so that the purpose of recommending the papers is achieved. Previous methods for citation-based paper recommendation are limited to using the degree of coincidence between a paper citation and a cited paper set, and similarity cannot be calculated for papers with a degree of coincidence of 0. The invention sufficiently utilizes the information of 'indirect reference' between the papers through the weight reference network between the papers, and then obtains the distributed vector expressing the position of the papers in the reference network by using a matrix decomposition method, and uses the inner product as the similarity expression between the papers.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an academic network visual presentation method and system based on jump between star cloud pictures.
The academic network visual presentation method based on the jump between the star cloud pictures provided by the invention comprises the following steps:
step M1: acquiring nodes and connecting edges of an academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers;
step M2: setting the color and size of the nodes and the color of the connecting edges according to the nodes and the connecting edges of the academic network, and storing the nodes, the connecting edges and corresponding attributes in a graph file;
step M3: the graph file is used for laying out an academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file;
step M4: storing the content in the graph file in a database;
step M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the academic network star cloud picture by using a mode of inter-picture jumping.
Preferably, the step M1 includes:
step M1.1: acquiring relevant data of the papers in the academic network from a database, wherein the relevant data comprises the paper ID of the node, the title of the papers and the publication date of the papers;
step M1.2: and acquiring reference relation data of the thesis from a database according to the acquired related data of the thesis in the academic network, and storing the related data of the node and the reference relation data in a memory.
Preferably, the step M2 includes:
step M2.1: setting the size of the node according to the reference quantity of the paper in the network;
step M2.2: screening node labels according to the sizes of the nodes, and deleting the node labels which do not accord with the preset node sizes;
step M2.3: setting the same color for the nodes in the same cluster according to the clustering relation among the nodes in the network by using the preset color, setting different colors for different clusters, and fusing the colors of the nodes at two ends of the continuous edge as the color of the continuous edge;
step M2.4: and storing the academic network nodes, the connecting edges and the corresponding attributes in a graph file.
Preferably, the step M3 includes:
step M3.1: reading the graph file, adjusting the nodes and the connecting edges in the graph file according to the related parameters of the layout algorithm to enable the nodes and the connecting edges in the graph file to meet the preset requirements, acquiring the two-dimensional coordinate position information of the nodes to enable the nodes and the connecting edges in the graph file to meet the preset requirements, and adding the two-dimensional coordinate position information into the graph file.
Preferably, the step M4 includes:
step M4.1: converting each graph file with a preset format into an sql file;
step M4.2: writing the sql file into the database.
Preferably, the step M5 includes: and acquiring a leading article star cloud picture of each academic network according to data in the database, and finishing visual presentation of the academic network star cloud pictures in a mode of inter-picture jumping.
The invention provides an academic network visual presentation system based on inter-star cloud picture jumping, which comprises:
module M1: acquiring nodes and connecting edges of an academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers;
module M2: setting the color and size of the nodes and the color of the connecting edges according to the nodes and the connecting edges of the academic network, and storing the nodes, the connecting edges and corresponding attributes in a graph file;
module M3: the graph file is used for laying out an academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file;
module M4: storing the content in the graph file in a database;
module M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the academic network star cloud picture by using a mode of inter-picture jumping.
Preferably, said module M1 comprises:
module M1.1: acquiring relevant data of the papers in the academic network from a database, wherein the relevant data comprises the paper ID of the node, the title of the papers and the publication date of the papers;
module M1.2: acquiring reference relation data of the thesis from a database according to the acquired related data of the thesis in the academic network, and storing the related data of the node and the reference relation data in a memory;
the module M2 includes:
module M2.1: setting the size of the node according to the reference quantity of the paper in the network;
module M2.2: screening node labels according to the sizes of the nodes, and deleting the node labels which do not accord with the preset node sizes;
module M2.3: setting the same color for the nodes in the same cluster according to the clustering relation among the nodes in the network by using the preset color, setting different colors for different clusters, and fusing the colors of the nodes at two ends of the continuous edge as the color of the continuous edge;
module M2.4: and storing the academic network nodes, the connecting edges and the corresponding attributes in a graph file.
Preferably, said module M3 comprises:
module M3.1: reading a graph file, adjusting nodes and connecting edges in the graph file according to related parameters of a layout algorithm to enable the nodes and the connecting edges in the graph file to meet preset requirements, acquiring two-dimensional coordinate position information of the nodes to enable the nodes and the connecting edges in the graph file to meet the preset requirements, and adding the two-dimensional coordinate position information into the graph file;
the module M4 includes:
module M4.1: converting each graph file with a preset format into an sql file;
module M4.2: writing the sql file into the database.
Preferably, said module M5 comprises: and acquiring a leading article star cloud picture of each academic network according to data in the database, and finishing visual presentation of the academic network star cloud pictures in a mode of inter-picture jumping.
Compared with the prior art, the invention has the following beneficial effects:
1. the method for visually presenting the mass academic networks based on the jump among the star cloud pictures can quickly visually present the mass academic networks led by the high-introduction papers in the whole field, so that the knowledge structure is visual.
2. According to the invention, the massive star cloud images are navigated and browsed in an inter-image jumping manner, so that the front-end visual presentation of the star cloud images becomes simple and efficient;
3. the invention has the advantages that the visual presentation of the planet cloud picture is carried out at the front end, and the interactive function of the picture is added, so that the user can perceive the academia in a map mode.
4. The invention quickly completes the layout of 47310 academic networks with the node number more than 1000 in a parallel mode, and completes the front-end visual presentation of the massive academic networks in a star-cloud graph mode in a 'jump between graphs' mode, so that the massive academic networks have visual and interactive big data.
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 is an example of 6 typical cloudiness maps;
FIG. 3 is a navigation page map of a mass star cloud map;
FIG. 4 is a diagram illustrating navigation satellite cloud inter-map hopping.
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.
Example 1
The academic network visual presentation method based on the jump between the star cloud pictures provided by the invention comprises the following steps:
step M1: acquiring nodes and connecting edges of a mass academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers;
step M2: setting the color and the size of the nodes and the color and the size of the connecting edges according to the nodes and the connecting edges of the academic network structure, and storing the nodes, the connecting edges and corresponding attributes in a graph file;
step M3: the method comprises the following steps that a graph file is used for laying out a massive academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file;
step M4: storing the content in the graph file in a database;
step M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the massive academic network star cloud picture by using a mode of inter-picture jumping.
Specifically, the step M1 includes:
step M1.1: acquiring relevant data of the papers in the academic network from a database, wherein the relevant data comprises the paper ID of the node, the title of the papers and the publication date of the papers;
step M1.2: and acquiring reference relation data of the thesis from a database according to the acquired related data of the thesis in the academic network, and storing the related data of the node and the reference relation data in a memory.
Specifically, the step M2 includes:
step M2.1: setting the size of the node according to the degree of the node in the network, namely, the reference amount of the thesis in the network, wherein the larger the degree of the node is, the larger the size is, and the sizes of the edges are uniformly set;
step M2.2: visual confusion is caused by too many node labels, the node labels are screened according to the sizes of the nodes, and the node labels with smaller incomes are deleted;
step M2.3: setting the same color for the nodes in the same cluster according to the clustering relation among the nodes in the network by using the preset color, setting different colors for different clusters, and fusing the colors of the nodes at two ends of the continuous edge as the color of the continuous edge;
step M2.4: and storing the academic network nodes, the connecting edges and the corresponding attributes in a graph file.
Specifically, the step M3 includes:
step M3.1: reading the graph file, adjusting the nodes and the connecting edges in the graph file according to the related parameters of the layout algorithm to enable the nodes and the connecting edges in the graph file to meet the preset requirements, acquiring the two-dimensional coordinate position information of the nodes to enable the nodes and the connecting edges in the graph file to meet the preset requirements, and adding the two-dimensional coordinate position information into the graph file.
Specifically, the step M4 includes:
step M4.1: converting each graph file with a preset format into an sql file for writing data into a database;
step M4.2: and writing the sql file into a database, thereby completing the storage of the visualization result.
Specifically, the step M5 includes: and acquiring a leading article star cloud picture of each academic network according to data in the database, and finishing visual presentation of the academic network star cloud pictures in a mode of inter-picture jumping.
Specifically, step M5.1: acquiring a leading article of each academic network and a reference relation among the articles, and taking the academic network completion layout formed by the articles as a navigation home page of a massive star cloud picture as shown in FIG. 3;
step M5.2: generating a thumbnail of each academic network star cloud picture for use as an inter-picture jump, as shown in fig. 2 and 4;
step M5.3: and navigation of massive star cloud pictures is completed at the front end in a picture-to-picture jumping mode.
The invention provides an academic network visual presentation system based on inter-star cloud picture jumping, which comprises:
module M1: acquiring nodes and connecting edges of a mass academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers;
module M2: setting the color and the size of the nodes and the color and the size of the connecting edges according to the nodes and the connecting edges of the academic network structure, and storing the nodes, the connecting edges and corresponding attributes in a graph file;
module M3: the method comprises the following steps that a graph file is used for laying out a massive academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file;
module M4: storing the content in the graph file in a database;
module M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the massive academic network star cloud picture by using a mode of inter-picture jumping.
Specifically, the module M1 includes:
module M1.1: acquiring relevant data of the papers in the academic network from a database, wherein the relevant data comprises the paper ID of the node, the title of the papers and the publication date of the papers;
module M1.2: and acquiring reference relation data of the thesis from a database according to the acquired related data of the thesis in the academic network, and storing the related data of the node and the reference relation data in a memory.
Specifically, the module M2 includes:
module M2.1: setting the size of the node according to the degree of the node in the network, namely, the reference amount of the thesis in the network, wherein the larger the degree of the node is, the larger the size is, and the sizes of the edges are uniformly set;
module M2.2: visual confusion is caused by too many node labels, the node labels are screened according to the sizes of the nodes, and the node labels with smaller incomes are deleted;
module M2.3: setting the same color for the nodes in the same cluster according to the clustering relation among the nodes in the network by using the preset color, setting different colors for different clusters, and fusing the colors of the nodes at two ends of the continuous edge as the color of the continuous edge;
module M2.4: and storing the academic network nodes, the connecting edges and the corresponding attributes in a graph file.
Specifically, the module M3 includes:
module M3.1: reading the graph file, adjusting the nodes and the connecting edges in the graph file according to the related parameters of the layout algorithm to enable the nodes and the connecting edges in the graph file to meet the preset requirements, acquiring the two-dimensional coordinate position information of the nodes to enable the nodes and the connecting edges in the graph file to meet the preset requirements, and adding the two-dimensional coordinate position information into the graph file.
Specifically, the module M4 includes:
module M4.1: converting each graph file with a preset format into an sql file for writing data into a database;
module M4.2: and writing the sql file into a database, thereby completing the storage of the visualization result.
Specifically, the module M5 includes: and acquiring a leading article star cloud picture of each academic network according to data in the database, and finishing visual presentation of the academic network star cloud pictures in a mode of inter-picture jumping.
Specifically, module M5.1: acquiring a leading article of each academic network and a reference relation among the articles, and taking the academic network completion layout formed by the articles as a navigation home page of the massive star cloud pictures;
module M5.2: generating a thumbnail of each academic network star cloud picture, and using the thumbnail as an inter-picture jump;
module M5.3: and navigation of massive star cloud pictures is completed at the front end in a picture-to-picture jumping mode.
Example 2
Example 2 is a modification of example 1
Taking 47310 academic networks led by high-influence papers with reference quantity exceeding 1000 in the academic full field as an example, the method and the system for visually presenting the massive academic networks based on inter-satellite-cloud-map jumping provided by the embodiment relate to the arrangement of the academic networks including acquisition of paper IDs and titles from a database, reference relations, setting of nodes and connection edge attributes based on a network structure, rapid distribution of satellite cloud maps by using an automatic layout tool, efficient storage of massive satellite cloud map data and visual presentation of the massive satellite cloud maps based on inter-map jumping; specifically, as shown in fig. 1, the method comprises the following steps:
step S1: acquiring nodes and connecting edges of a mass academic network, namely related papers and reference relations among the related papers from a database;
step S2: setting the color and size of the node and the color and size of the connecting edge according to the academic network structure, and storing the node connecting edge and the attribute thereof in a graph file;
step S3: the massive network is laid out in a multi-process parallel mode to determine the position of a node in a two-dimensional space, and position information is added into a graph file;
step S4: storing the content in the final graph file in a database;
step S5: and generating a star cloud picture at the front end in real time, and finishing the front-end visual presentation of the massive academic network star cloud picture by using a 'jump between pictures'.
Step S1 includes: and acquiring nodes and edges of the massive academic networks, namely related papers and reference relations among the related papers from the database. 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 publication date of the papers in the academic network led by 47310 high-lead papers in the whole field are obtained from the aceap database. After the relevant information of the thesis is acquired, the thesis information is stored in the memory in the following format for later use:
[{“paper_id”:paper_id0,“title”:title0},{“paper_id”:paper_id1,“title”:title1},…]
where "paper _ ID" is used for the index paper ID and "title" is used for the index paper title.
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 refers to the "target" paper.
Step S2 includes: and setting the sizes of the nodes and the edges according to the degree of the nodes in the network, and deleting the labels of part of the nodes. Then setting colors for the nodes according to the similarity of the reference relations of the nodes, and finally storing the academic network containing the attributes in a graph file, specifically:
step S201: according to academic network information acquired from the aceap, setting the sizes of the network nodes and the connecting edges according to the following steps:
in a particular academic network, all nodes therein are traversed to obtain the maximum In-Degree Max In Degree of the nodes In the network.
Assume a particular academic network has a total number of nodes NsumThen calculate the graph based on this and the following empirical formula
Size of the medium maximum node:
suppose the In Degree of node n is In DegreenThen each section in the academic network is calculated according to the following empirical formula
Dot size:
the size of the connecting edge is uniformly set to be 1.0.
Step S202: for academic networks formed by academic papers, the titles of the papers tend to be long, and if all of them are displayed in the network, the aesthetic level is greatly reduced. Therefore, in this step, according to the degree of income of the node, the label of the node with smaller degree of income, that is, the title of the corresponding thesis, is deleted, and the specific steps are as follows:
in a networkThe nodes are arranged in descending order according to the degree of entry, and the ordered node IDs are stored into a List Listin-networkIn (1).
Suppose the number of nodes in the network that need to retain labels is NTitl netThen, the node number of the reserved label is calculated according to the following empirical formula:
keeping List in the star cloud picture according to the principle of keeping the node title with larger income degreein-networkMiddle front NTitl netThe title of each node, thereby completing the deletion of the node title.
Step S203: in order to distinguish the plate structure inside the cloudiness picture, predetermined colors are used, clustering is carried out according to the citation relation of a thesis, the same colors are set for the nodes of the same cluster, different colors are set for the nodes of different clusters, and the colors of the nodes are mixed to set the color of a continuous edge, and the specific steps are as follows:
to highlight the leading article of the star cloud picture, its color is set separately as red, and the RGB value is # fc 0706.
Setting of colors of other nodes in the article:
and arranging the nodes of the articles except the leading articles 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 nodes which quote the thesis. And the node with the highest current in-degree and the node which refers to the paper are assigned with the same color in a preset color list to represent the relevance of the reference relation. The above steps are repeated 7 times to complete the coloring of most articles in the network, and in particular, the predetermined color list is as follows:
after coloring of most nodes is finished, the rest nodes are set to be uniform blue background color, and the RGB value is #2e5 bff.
Calculation of edge color:
the color of the edge is set by mixing the RGB values of the nodes at two sides of the edge, and the RGB values of the node 1 and the node 2 are assumed to be R respectively1，G1，B1And R2，G2，B2Then the RGB values of the edges are calculated by:
step S204: taking a paper in an academic network led by each high-influence article as a node, a reference relation as a continuous edge, and a leading article ID as a file name, writing size information of the node and the continuous edge obtained in the step S201, label information of the node in the network obtained in the step S202, and colors of the node and the continuous edge obtained in the step S203 into a graph file, and storing the graph file in the form of a gll file according to the following format:
the meaning represented by each field is illustrated by the "#" sign of that field followed by a suffix.
Step S3 includes: and performing rapid layout on the massive academic networks in parallel by using gephi-tool to obtain the layout of each star cloud graph, and generating a graph file containing node position information, wherein part of typical star cloud graph layout effects are shown in fig. 2. 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 academic network to which the high-influence paper is led obtained in step S2, layout is performed using gephi-tool, and the graph file including the position information after the 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: firstly, each graph file with a preset format is converted into an SQL file, wherein the SQL file comprises SQL sentences which add elements to a database table. After the generation of the sql file is completed, writing data into the database through the sql file, specifically:
step S401: all the gml files are read and the information in these files is stored in node. Sql is used for storing information of all nodes in the gml files, edge is used for storing information of all connected edges in the gml files, info is used for counting information of the star cloud picture, and the content of each sql file is as follows:
node.sql：
INSERT INTO map_node(field_id,paper_id,year,x,y,radius,fill,type,label,label_size)VALUES(306440788,306440788,1966,-156.70181,-108.52587,175.0,'fc0706','seminal_paper_map','Internal labor markets and manpower analysis',52.5)；
the field _ ID is used for indexing the whole network, values are often the paper ID of a leading article, paper _ ID represents the ID of a paper corresponding to a node in the network, year represents the published year of the paper, x and y represent the position of the node on a two-dimensional plane, radius represents the radius of the node, file represents the color of node filling, type represents the category of the academic network, label represents the title of the paper, and label _ size represents the size of the title of the paper.
edge.sql：
INSERT INTO map_edge(field_id,source_id,target_id,width,stroke,type)VALUES(306440788,396209106,312470281,1.0,'ff9933','seminal_paper_map')；
The field _ ID is used for indexing the whole network, the value is often the paper ID of a leading article, source _ ID represents the ID of a source node connected with an edge, target _ ID represents the ID of a target node connected with the edge, the meaning of the edge is that source _ ID refers to target _ ID, width represents the line width of the connected edge, stroke represents the color of the connected edge, and type represents the category of the academic network.
info.sql：
INSERT INTO map_information(field_id,type,introduction)VALUES(306440788,'seminal_paper_map','Here you can see the citation relationship of seminal paper and papers that references it.')；
Where field _ id is used to index the entire network, type represents the category of academic network, and introduction represents the introduction to the academic network.
Step S402: using MySQL to read node, edge, sql, info, sql, so as to write all the information in the gml file into three tables of map _ node, map _ edge, and map _ information of the database, thereby completing the storage of the visualization result, specifically, the table structures of these three tables are as follows:
map_node：
map_edge：
| field_id | source_id | target_id | width | type |
| 306440788 | 396209106 | 312470281 | 1.0 | seminal_paper_map |
map_information：
step S5 includes: and acquiring leading articles of all the star cloud pictures and visualizing the quoting relation among the articles into the star cloud pictures to serve as navigation pages browsed by massive star cloud pictures. In addition, a thumbnail of each star cloud picture is generated, so that inter-picture jumping is completed by clicking a front-end thumbnail, specifically:
step S501: the IDs of all the articles to be led are acquired, and the reference relationship between them will be acquired in the same step as S102. Then, according to the step S2, the setting of the network node and edge attributes is completed, and the layout of the network is completed according to the step S3, and finally, according to the step S4, the network is written into the database according to the field _ id of 0, and the front end of the navigation star cloud map is shown in fig. 3.
Step S502: and using a gml2png tool in the gephi-tool to complete the conversion of the gml file of the star cloud picture into the png bitmap for thumbnail clicking of inter-picture jumping.
Step S503: each Star cloud graph is rendered at the front end using the entity page of a particular graph, each entity page being linked using a URL containing the field _ id of the graph. When the star cloud image is presented, three tables of map _ node, map _ edge and map _ information are respectively inquired in sequence, so that the nodes of the star cloud image corresponding to the field _ id are obtained, all information of the edges are connected, and SVG is generated at the front end in real time so as to finish presentation. In addition, binding click events to all nodes in the network, after a certain node is clicked, inquiring a field _ id star cloud graph corresponding to the paper _ id or a star cloud graph containing the paper _ id, displaying a thumbnail of the star cloud graph in an upper left corner card, and clicking the thumbnail to complete the realization of inter-graph jump logic. In addition, browsing and accessing of 47310 articles can be completed by clicking on the navigation star cloud map, and a specific inter-map jump schematic diagram is shown in fig. 4.
Firstly, the mass academic network visual presentation method based on the inter-satellite-cloud-picture skip can quickly complete the visual presentation of the mass academic network led by the high-lead thesis in the whole field, so that the knowledge structure is visual and intuitive. Secondly, the invention navigates and browses the massive star cloud images in a mode of 'jumping between images', so that the front-end visual presentation of the star cloud images becomes simple and efficient; finally, the visual presentation of the planet cloud picture is carried out at the front end, and the interactive function of the picture is added, so that the user can perceive the academia in a map mode.
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. An academic network visual presentation method based on jump between star cloud pictures is characterized by comprising the following steps:
step M1: acquiring nodes and connecting edges of an academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers;
step M2: setting the color and size of the nodes and the color of the connecting edges according to the nodes and the connecting edges of the academic network, and storing the nodes, the connecting edges and corresponding attributes in a graph file;
step M3: the graph file is used for laying out an academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file;
step M4: storing the content in the graph file in a database;
step M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the academic network star cloud picture by using a mode of inter-picture jumping.
2. The academic network visual presentation method based on inter-satellite-cloud-map jumping as claimed in claim 1, wherein the step M1 comprises:
step M1.1: acquiring relevant data of the papers in the academic network from a database, wherein the relevant data comprises the paper ID of the node, the title of the papers and the publication date of the papers;
step M1.2: and acquiring reference relation data of the thesis from a database according to the acquired related data of the thesis in the academic network, and storing the related data of the node and the reference relation data in a memory.
3. The academic network visual presentation method based on inter-satellite-cloud-map jumping as claimed in claim 1, wherein the step M2 comprises:
step M2.1: setting the size of the node according to the reference quantity of the paper in the network;
step M2.2: screening node labels according to the sizes of the nodes, and deleting the node labels which do not accord with the preset node sizes;
step M2.3: setting the same color for the nodes in the same cluster according to the clustering relation among the nodes in the network by using the preset color, setting different colors for different clusters, and fusing the colors of the nodes at two ends of the continuous edge as the color of the continuous edge;
step M2.4: and storing the academic network nodes, the connecting edges and the corresponding attributes in a graph file.
4. The academic network visual presentation method based on inter-satellite-cloud-map jumping as claimed in claim 1, wherein the step M3 comprises:
step M3.1: reading the graph file, adjusting the nodes and the connecting edges in the graph file according to the related parameters of the layout algorithm to enable the nodes and the connecting edges in the graph file to meet the preset requirements, acquiring the two-dimensional coordinate position information of the nodes to enable the nodes and the connecting edges in the graph file to meet the preset requirements, and adding the two-dimensional coordinate position information into the graph file.
5. The academic network visual presentation method based on inter-satellite-cloud-map jumping as claimed in claim 1, wherein the step M4 comprises:
step M4.1: converting each graph file with a preset format into an sql file;
step M4.2: writing the sql file into the database.
6. The academic network visual presentation method based on inter-satellite-cloud-map jumping as claimed in claim 1, wherein the step M5 comprises: and acquiring a leading article star cloud picture of each academic network according to data in the database, and finishing visual presentation of the academic network star cloud pictures in a mode of inter-picture jumping.
7. An academic network visual presentation system based on jump between star cloud pictures is characterized by comprising:
module M1: acquiring nodes and connecting edges of an academic network from a database, wherein the nodes are papers, and the connecting edges are reference relations among the papers;
module M2: setting the color and size of the nodes and the color of the connecting edges according to the nodes and the connecting edges of the academic network, and storing the nodes, the connecting edges and corresponding attributes in a graph file;
module M3: the graph file is used for laying out an academic network in a multi-process parallel mode, the position of a node in a two-dimensional space is determined, and position information is added into the graph file;
module M4: storing the content in the graph file in a database;
module M5: and generating a star cloud picture in real time according to data in the database, and finishing the front-end visual presentation of the academic network star cloud picture by using a mode of inter-picture jumping.
8. The academic network visual presentation system based on inter-satellite-cloud-map jumping as claimed in claim 7, wherein the module M1 comprises:
module M1.1: acquiring relevant data of the papers in the academic network from a database, wherein the relevant data comprises the paper ID of the node, the title of the papers and the publication date of the papers;
module M1.2: acquiring reference relation data of the thesis from a database according to the acquired related data of the thesis in the academic network, and storing the related data of the node and the reference relation data in a memory;
the module M2 includes:
module M2.1: setting the size of the node according to the reference quantity of the paper in the network;
module M2.2: screening node labels according to the sizes of the nodes, and deleting the node labels which do not accord with the preset node sizes;
module M2.3: setting the same color for the nodes in the same cluster according to the clustering relation among the nodes in the network by using the preset color, setting different colors for different clusters, and fusing the colors of the nodes at two ends of the continuous edge as the color of the continuous edge;
module M2.4: and storing the academic network nodes, the connecting edges and the corresponding attributes in a graph file.
9. The academic network visual presentation system based on inter-satellite-cloud-map jumping as claimed in claim 7, wherein the module M3 comprises:
module M3.1: reading a graph file, adjusting nodes and connecting edges in the graph file according to related parameters of a layout algorithm to enable the nodes and the connecting edges in the graph file to meet preset requirements, acquiring two-dimensional coordinate position information of the nodes to enable the nodes and the connecting edges in the graph file to meet the preset requirements, and adding the two-dimensional coordinate position information into the graph file;
the module M4 includes:
module M4.1: converting each graph file with a preset format into an sql file;
module M4.2: writing the sql file into the database.
10. The academic network visual presentation system based on inter-satellite-cloud-map jumping as claimed in claim 7, wherein the module M5 comprises: and acquiring a leading article star cloud picture of each academic network according to data in the database, and finishing visual presentation of the academic network star cloud pictures in a mode of inter-picture jumping.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105718528A (en) * | 2016-01-15 | 2016-06-29 | 上海交通大学 | Academic map display method based on reference relationship among thesises |
| CN111309917A (en) * | 2020-03-11 | 2020-06-19 | 上海交通大学 | Super-large scale academic network visualization method and system based on conference periodical galaxy diagram |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105718528A (en) * | 2016-01-15 | 2016-06-29 | 上海交通大学 | Academic map display method based on reference relationship among thesises |
| CN111309917A (en) * | 2020-03-11 | 2020-06-19 | 上海交通大学 | Super-large scale academic network visualization method and system based on conference periodical galaxy diagram |
Non-Patent Citations (1)
| Title |
|---|
| ACEMAP上海交通大学: ""知识结构"初探 | 学术全领域47310张星云图可视呈现与结构分析", 《HTTPS://ZHUANLAN.ZHIHU.COM/P/186297757》 * |
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