CN111897973A - WebGL-based mass node knowledge graph visual layout method and system - Google Patents
WebGL-based mass node knowledge graph visual layout method and system Download PDFInfo
- Publication number
- CN111897973A CN111897973A CN202010794767.1A CN202010794767A CN111897973A CN 111897973 A CN111897973 A CN 111897973A CN 202010794767 A CN202010794767 A CN 202010794767A CN 111897973 A CN111897973 A CN 111897973A
- Authority
- CN
- China
- Prior art keywords
- knowledge
- graph
- nodes
- node
- knowledge graph
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000000007 visual effect Effects 0.000 title claims abstract description 10
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000012800 visualization Methods 0.000 claims description 20
- 239000002245 particle Substances 0.000 claims description 6
- 238000000137 annealing Methods 0.000 claims description 4
- 238000009877 rendering Methods 0.000 description 7
- 230000001133 acceleration Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013515 script Methods 0.000 description 1
- 230000009469 supplementation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/34—Browsing; Visualisation therefor
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
In order to solve the problem of low layout efficiency of the traditional force-oriented layout algorithm, the mass node knowledge graph visual layout method and system based on the WebGL are provided, and the layout efficiency is improved; the method comprises the following steps: step S1, acquiring knowledge graph nodes, and step S2, allocating an initial position to each knowledge graph node; step S3, acquiring an adjacent node set of each knowledge-graph node, and calculating the attraction and the repulsion of each knowledge-graph node according to the position of each knowledge-graph node and the position of each node in the adjacent node set; step S4, adjusting the position of the knowledge-graph nodes according to the attractive force and the repulsive force of each knowledge-graph node; and repeating the steps S3-S4 until the repetition times reach a first preset threshold or the attractive force and the repulsive force of the knowledge graph nodes reach balance. The application also discloses a corresponding system and computer equipment, and the method, the system and the equipment can improve the efficiency of the visual layout of the knowledge graph.
Description
Technical Field
The disclosure relates to the field of knowledge graph visualization, in particular to a mass node knowledge graph visualization layout method and system based on WebGL.
Background
The visual layout is used for processing the rendering of the visual layout of the network map, and is an intuitive and visual method for displaying the relationship tightness between the nodes and displaying the relationship between the nodes through edges. The knowledge graph visualization relates to nodes, edges and visual effects, a user can conveniently explore and understand problems, the conventional force guiding layout is adopted in the conventional knowledge graph node layout, and the defects that when the knowledge graph relates to nodes with a large mass scale, the conventional force guiding layout algorithm is high in complexity, and the layout efficiency is low are solved.
Disclosure of Invention
In order to solve at least one of the technical problems, the present disclosure provides a mass node knowledge graph visualization layout method and system based on WebGL, which improve the efficiency of mass node knowledge graph visualization layout based on WebGL.
In a first aspect of the disclosure, a WebGL-based massive node knowledge graph visualization layout method includes:
step S1: acquiring nodes of a knowledge graph;
step S2: assigning an initial position to each of the knowledge-graph nodes;
step S3: calculating the attractive force and repulsive force of each knowledge-graph node according to the position of the knowledge-graph node and the position of each node in the adjacent node set;
step S4: adjusting the positions of the knowledge-graph nodes according to the attractive force and the repulsive force of each knowledge-graph node;
and repeating the steps S3-S4 until the repetition times reach a first preset threshold value or the attractive force and the repulsive force of each knowledge-graph node reach balance.
Optionally, the set of nodes adjacent to the nodes of the knowledge-graph is a set of nodes near to the front M of the nodes of the knowledge-graph, where M is smaller than the total number of nodes of the knowledge-graph.
Optionally, M is equal to
Wherein N is the total number of nodes of the knowledge graph.
Optionally, the calculating, according to the positions of the nodes of the knowledge-graph and the positions of each node in the set of adjacent nodes, an attractive force and a repulsive force of each node of the knowledge-graph includes:
acquiring the distance between the nodes of the knowledge graph and each node in the adjacent node set according to the positions of the nodes of the knowledge graph and the positions of each node in the adjacent node set;
calculating attractive and repulsive forces for each of the knowledge-graph nodes according to the following formulas;
wherein fa is the attraction of the nodes of the knowledge-graph, fr is the repulsion of the nodes of the knowledge-graph, M is the number of nodes in the set of adjacent nodes of the knowledge-graph, DmAnd expressing the distance between the nodes of the knowledge graph and the mth adjacent node in the adjacent node set, wherein K is the preset optimal distance between the nodes.
Optionally, whether the attraction force and the repulsion force of each knowledge-graph node reach balance is judged according to whether the difference between the attraction force and the repulsion force of each knowledge-graph node is smaller than a second preset threshold. .
Optionally, the second preset threshold is |0.0000001 × (F-0.5) |, where F is a random number between 0 and 1.
Optionally, when the position of the knowledge-graph node is adjusted, the maximum moving distance of the knowledge-graph node is dynamically adjusted by an annealing algorithm.
Optionally, when the position of the knowledgegraph node is adjusted, a plurality of moving frames are inserted between the start position and the end position of the movement of the knowledgegraph node according to time equal division.
Optionally, the steps S3 to S4 are executed by a WebWorker multithread, and the knowledge graph is rendered by a particle system of WebGL.
In a second aspect of the present disclosure, a WebGL-based massive node knowledge graph visualization layout system includes:
the acquisition module is used for acquiring nodes of the knowledge graph;
the position distribution module is used for distributing an initial position for each knowledge-graph node;
the layout module is used for calculating the attraction force and the repulsion force of each knowledge-graph node according to the position of the knowledge-graph node and the position of each node in the adjacent node set;
the adjusting module is used for adjusting the positions of the knowledge-graph nodes according to the attractive force and the repulsive force of each knowledge-graph node;
and the control module is used for controlling the layout module and the adjusting module to be repeatedly executed until the repetition times reach a first preset threshold value or the attractive force and the repulsive force of each knowledge graph node reach balance.
In a third aspect of the disclosure, a computer device comprises a processor and a memory, wherein at least one instruction is stored, loaded and executed by the processor to implement the method according to any one of the first aspect of the disclosure.
The technical scheme of the present disclosure can be implemented to obtain the following beneficial technical effects: the method comprises the steps of calculating attractive force and repulsive force of nodes of the knowledge graph based on adjacent node sets of the nodes of the knowledge graph, and adjusting the positions of the nodes of the knowledge graph according to the attractive force and the repulsive force of the nodes of the knowledge graph, so that compared with a traditional force guide graph layout mode, the calculation amount can be reduced, and the layout efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a WebGL-based massive node knowledge graph visualization layout method in an embodiment of the present disclosure.
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that, in a non-conflicting manner, the embodiments and the features of the embodiments in the present disclosure may be combined with each other, and the implementation subject of the method in the present embodiment may be adjusted according to specific cases, such as a server, an electronic device, a computer, and the like. .
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a WebGL-based massive node knowledge graph visualization layout method includes:
step S1: acquiring nodes of a knowledge graph;
step S2: assigning an initial position to each knowledge-graph node;
step S3: calculating the attractive force and repulsive force of each knowledge-graph node according to the position of the knowledge-graph node and the position of each node in the adjacent node set;
step S4: adjusting the positions of the knowledge-graph nodes according to the attractive force and the repulsive force of each knowledge-graph node;
and repeating the steps S3-S4 until the repetition number reaches a first preset threshold or the attractive force and the repulsive force of each knowledge-graph node reach balance.
Wherein, the initial position in step S2 may be assigned by a random strategy consistent with the force guide graph; the set of adjacent nodes of a knowledge-graph node is a set of adjacent nodes of the knowledge-graph node, where the adjacent nodes are less than the total number of all nodes relative to all nodes; the first preset threshold can be set according to specific needs, and is the maximum iteration step number for adjusting the positions of the nodes of the knowledge graph; when the acceleration of the knowledge graph nodes after being stressed (attractive force and repulsive force) is smaller than the acceleration threshold, the energy of the system can be considered to be smaller than the energy threshold, and at the moment, the attractive force and the repulsive force of the knowledge graph nodes can be considered to be balanced.
It can be appreciated that, when step S3 is performed for the first time, the location of the knowledgegraph node in step S3 and the location of each node in its set of neighboring nodes are the initial locations of the respective knowledgegraph nodes assigned in step S2; after repeatedly performing step S3, the position of each node in the set of nodes and its neighbors in step S3 is the position of the corresponding knowledgegraph node after adjusting the position of the knowledgegraph node in step S4.
In the WebGL-based massive node knowledge graph visualization layout method, the attraction force and the repulsion force of the nodes of the knowledge graph are calculated based on the adjacent node sets of the nodes of the knowledge graph, the positions of the nodes of the knowledge graph are adjusted according to the attraction force and the repulsion force of the nodes of the knowledge graph, and the traditional force guide graph layout mode is based on the mode that the nodes of the knowledge graph and all other nodes calculate the attraction force and the repulsion force of the nodes of the knowledge graph.
Specifically, the adjacent node set of the nodes of the knowledge graph is a set of nodes which are close to the front M of the nodes of the knowledge graph, wherein M is smaller than the total number of the nodes of the knowledge graph.
In an alternative embodiment, M is equal to
Wherein N is the total number of nodes of the knowledge graph; m is the total number of adjacent nodes in the adjacent node set, wherein N is the total number of nodes of the knowledge graph; book (I)Examples M adopt
N/500, (N/100-400) the maximum of these three values; on one hand, the method can be conveniently executed by a computer, and on the other hand, the M value can be automatically adjusted according to the number of nodes of the knowledge graph, so that the reduced calculated amount is different under the condition that the number of the nodes of the knowledge graph is different in order of magnitude, and the calculated amount and the layout precision can be considered. Wherein the above [ 2 ]]To represent
The integer part of (2).
In an alternative embodiment, calculating the attraction and repulsion of each knowledge-graph node based on the position of the knowledge-graph node and the position of each node in its set of neighboring nodes comprises:
acquiring the distance between the nodes of the knowledge graph and each node in the adjacent node sets according to the positions of the nodes of the knowledge graph and the positions of each node in the adjacent node sets;
calculating the attractive and repulsive forces of each knowledge-graph node according to the following formula;
wherein fa is the attraction of the nodes of the knowledge-graph, fr is the repulsion of the nodes of the knowledge-graph, M is the number of nodes in the set of adjacent nodes of the knowledge-graph, DmAnd expressing the distance between the nodes of the knowledge graph and the mth adjacent node in the adjacent node set, wherein K is the preset optimal distance between the nodes.
By the method, the relation between the distance between the knowledge graph node and each node in the adjacent node set and the optimal distance can be converted into the attraction force and the repulsion force of the knowledge graph node, so that the node motion is simulated according to the attraction force and the repulsion force, and the specific adjustment of the knowledge graph node to the optimal distance is performed step by step.
In another possible implementation manner, whether the attraction force and the repulsion force of each knowledge-graph node reach balance is judged according to whether the difference between the attraction force and the repulsion force of each knowledge-graph node is smaller than a second preset threshold value.
The specific second preset threshold is |0.0000001 × (F-0.5) |, where F is a random number between 0 and 1. And recalculating the second preset threshold value every time step S4 is executed, so that the second preset threshold value is randomly changed, and ensuring the execution of the method.
When the positions of the nodes of the knowledge graph are adjusted, the maximum moving distance of the nodes of the knowledge graph can be dynamically adjusted by an annealing algorithm. Specifically, the positions of the nodes of the knowledge graph can be directly adjusted by adopting an annealing algorithm.
When the positions of the knowledge-graph nodes are adjusted, a plurality of moving frames can be equally inserted between the moving start position and the moving end position of the knowledge-graph nodes according to time. By inserting a plurality of moving frames equally by time between the start position and the end position of the node movement, the picture change process can be made to look smoother, the smoothness is improved in visual effect, and the user feels smoother.
In an alternative embodiment, steps S3-S4 are performed using WebWorker multithreading; a WebWorker multithreading technology is adopted, the multithreading technology is a new characteristic supported by a modern browser, and the layout algorithm can be asynchronously and parallelly operated by adopting the characteristic, so that the problem of improving the layout efficiency is solved.
In an alternative embodiment, the particle system of WebGL is used to render the knowledgegraph. The WebGL image rendering engine is more efficient than a conventional Canvas, rendering is performed by adopting a WebGL particle system, rendering efficiency can be effectively improved, the particle system is much simpler than a geometric object, and the particle system is supported for large-scale rendering only by adding texture maps to simple position information. The GPGPU is used for parallel computing, a CPU is stronger than the GPU in single-task computing processing, but the GPU can perform concurrent computing by utilizing the advantages of multiple simple kernels in large-scale concurrent computing to achieve the problem of improving the operation efficiency, and the GPU is used for computing the layout, so that the performance can be greatly improved.
In order to solve the problem of unpacking the data, the method adopts an ArrayBuffer content sharing mode, so that the problem of unpacking the data is effectively solved, and the position obtained by a layout algorithm can be quickly transmitted to a renderer.
The webworker multithreading refers to browser multithreading, because a browser can provide multiple instances of js engines, each instance can independently run a corresponding program, but the js scripts in each js engine instance are executed in a single thread, and each instance is equivalent to one webworker. Wherein js refers to JavaScript.
The WebGL is called Web Graphics Library entirely and is a 3D drawing protocol.
The CPU is called a central processing unit, and refers to a central processing unit.
The GPU is referred to as a Graphics Processing Unit, and refers to a Graphics processor.
The ArrayBuffer is also called a typed array, and is an original buffer area of binary data.
According to the method, due to the adoption of the integration of the method, the efficiency of layout is improved by multiple levels, so that compared with the efficiency of a traditional layout algorithm, the efficiency of the method can be improved by more than 1000 times, the layout time is greatly shortened, and meanwhile, the whole rendering effect is optimized by means of space supplementation and rendering, so that better performance is improved.
The application also discloses a mass node knowledge graph visualization layout system based on WebGL, which comprises:
the acquisition module is used for acquiring nodes of the knowledge graph;
a position assignment module: the knowledge-graph nodes are used for being distributed with an initial position;
a layout module: the device comprises a knowledge-graph node set, a plurality of nodes and a plurality of groups of nodes, wherein the knowledge-graph node set is used for calculating the attraction and the repulsion of each knowledge-graph node according to the position of the knowledge-graph node and the position of each node in the adjacent node set;
an adjusting module: the position of the knowledge-graph nodes is adjusted according to the attractive force and the repulsive force of each knowledge-graph node;
and the control module is used for controlling the layout module and the adjusting module to be repeatedly executed until the repetition times reach a first preset threshold value or the attractive force and the repulsive force of each knowledge graph node reach balance.
The application also discloses a computer device, which comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement any one of the WebGL-based massive node knowledge graph visualization layout methods in the embodiment.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of illustration of the disclosure and are not intended to limit the scope of the disclosure. Other variations or modifications may occur to those skilled in the art, based on the foregoing disclosure, and are still within the scope of the present disclosure.
Claims (10)
1. A mass node knowledge graph visualization layout method based on WebGL is characterized by comprising the following steps:
step S1: acquiring nodes of a knowledge graph;
step S2: assigning an initial position to each of the knowledge-graph nodes;
step S3: calculating the attractive force and repulsive force of each knowledge-graph node according to the position of the knowledge-graph node and the position of each node in the adjacent node set;
step S4: adjusting the positions of the knowledge-graph nodes according to the attractive force and the repulsive force of each knowledge-graph node;
and repeating the steps S3-S4 until the repetition times reach a first preset threshold value or the attractive force and the repulsive force of each knowledge-graph node reach balance.
2. The WebGL-based visualization layout method for the massive node knowledge graph as claimed in claim 1, wherein the set of nodes adjacent to the nodes of the knowledge graph is a set of nodes which are close to M before the nodes of the knowledge graph, wherein M is smaller than the total number of nodes of the knowledge graph.
3. The WebGL-based visualization layout method for massive node knowledge graph as claimed in claim 2, wherein M is equal to M
N/500, N/100 and 400, wherein N is the total number of nodes of the knowledge-graph.
4. The WebGL-based visualization layout method for the knowledge graph of the mass nodes as claimed in claim 2, wherein the step of calculating the attractive force and the repulsive force of each knowledge graph node according to the position of the knowledge graph node and the position of each node in the adjacent node set comprises the steps of:
acquiring the distance between the nodes of the knowledge graph and each node in the adjacent node set according to the positions of the nodes of the knowledge graph and the positions of each node in the adjacent node set;
calculating attractive and repulsive forces for each of the knowledge-graph nodes according to the following formulas;
wherein fa is the attraction of the nodes of the knowledge-graph, fr is the repulsion of the nodes of the knowledge-graph, M is the number of nodes in the set of adjacent nodes of the knowledge-graph, DmAnd expressing the distance between the nodes of the knowledge graph and the mth adjacent node in the adjacent node set, wherein K is the preset optimal distance between the nodes.
5. The WebGL-based visual layout method for massive node knowledge graphs as claimed in claim 1, wherein whether the attraction force and the repulsion force of each knowledge graph node are balanced is judged according to whether the difference between the attraction force and the repulsion force of each knowledge graph node is smaller than a second preset threshold value.
6. The WebGL-based visualization layout method for the knowledge graph of the mass nodes, as claimed in claim 1, wherein when the positions of the knowledge graph nodes are adjusted, the maximum moving distance of the knowledge graph nodes is dynamically adjusted by an annealing algorithm.
7. The WebGL-based visualization layout method for the knowledge graph of mass nodes as claimed in claim 6, wherein when the positions of the knowledge graph nodes are adjusted, a plurality of moving frames are inserted between the starting position and the ending position of the movement of the knowledge graph nodes according to time equal division.
8. The WebGL-based visualization layout method for the massive node knowledge graph as claimed in claim 1, wherein the steps S3-S4 are executed in a WebWorker multithreading mode, and the WebGL particle system is adopted to render the knowledge graph.
9. A mass node knowledge graph visualization layout system based on WebGL is characterized by comprising the following components:
the acquisition module is used for acquiring nodes of the knowledge graph;
a position assignment module: the knowledge-graph nodes are used for being distributed with an initial position;
a layout module: the device comprises a knowledge-graph node set, a plurality of nodes and a plurality of groups of nodes, wherein the knowledge-graph node set is used for calculating the attraction and the repulsion of each knowledge-graph node according to the position of the knowledge-graph node and the position of each node in the adjacent node set;
an adjusting module: the position of the knowledge-graph nodes is adjusted according to the attractive force and the repulsive force of each knowledge-graph node;
and the control module is used for controlling the layout module and the adjusting module to be repeatedly executed until the repetition times reach a first preset threshold value or the attractive force and the repulsive force of each knowledge graph node reach balance.
10. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction that is loaded and executed by the processor to implement the method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010794767.1A CN111897973A (en) | 2020-08-10 | 2020-08-10 | WebGL-based mass node knowledge graph visual layout method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010794767.1A CN111897973A (en) | 2020-08-10 | 2020-08-10 | WebGL-based mass node knowledge graph visual layout method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111897973A true CN111897973A (en) | 2020-11-06 |
Family
ID=73246646
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010794767.1A Pending CN111897973A (en) | 2020-08-10 | 2020-08-10 | WebGL-based mass node knowledge graph visual layout method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111897973A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112597317A (en) * | 2021-01-11 | 2021-04-02 | 西藏民族大学 | Knowledge graph visualization method and system |
| CN112966098A (en) * | 2021-03-15 | 2021-06-15 | 中国美术学院 | Product and matching supply relation visualization method and system thereof |
| CN112989028A (en) * | 2021-02-26 | 2021-06-18 | 江苏大学 | Knowledge graph layout optimization method based on force-oriented algorithm |
| CN114036700A (en) * | 2021-10-27 | 2022-02-11 | 中南大学 | Layout method of network asset graph |
| WO2022268118A1 (en) * | 2021-06-22 | 2022-12-29 | 北京锐安科技有限公司 | Adjustment method and apparatus for relationship diagram, and storage medium |
| CN116502716A (en) * | 2023-06-27 | 2023-07-28 | 深圳大学 | Knowledge graph layout method, device, equipment and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780072A (en) * | 2017-01-06 | 2017-05-31 | 上海前隆金融信息服务有限公司 | Power is oriented to self adaptation figure layout method and system |
| WO2019057190A1 (en) * | 2017-09-25 | 2019-03-28 | 腾讯科技（深圳）有限公司 | Method and apparatus for displaying knowledge graph, terminal device, and readable storage medium |
| CN109976735A (en) * | 2019-03-13 | 2019-07-05 | 中译语通科技股份有限公司 | One kind being based on the visual knowledge mapping algorithm application platform of web |
| CN111383312A (en) * | 2020-03-20 | 2020-07-07 | 厦门渊亭信息科技有限公司 | Large-scale knowledge graph visualization method and device based on particle system |
-
2020
- 2020-08-10 CN CN202010794767.1A patent/CN111897973A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780072A (en) * | 2017-01-06 | 2017-05-31 | 上海前隆金融信息服务有限公司 | Power is oriented to self adaptation figure layout method and system |
| WO2019057190A1 (en) * | 2017-09-25 | 2019-03-28 | 腾讯科技（深圳）有限公司 | Method and apparatus for displaying knowledge graph, terminal device, and readable storage medium |
| CN109976735A (en) * | 2019-03-13 | 2019-07-05 | 中译语通科技股份有限公司 | One kind being based on the visual knowledge mapping algorithm application platform of web |
| CN111383312A (en) * | 2020-03-20 | 2020-07-07 | 厦门渊亭信息科技有限公司 | Large-scale knowledge graph visualization method and device based on particle system |
Non-Patent Citations (1)
| Title |
|---|
| 程致远等: "面向大规模图数据的并行图布局算法", 《大数据》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112597317A (en) * | 2021-01-11 | 2021-04-02 | 西藏民族大学 | Knowledge graph visualization method and system |
| CN112989028A (en) * | 2021-02-26 | 2021-06-18 | 江苏大学 | Knowledge graph layout optimization method based on force-oriented algorithm |
| CN112966098A (en) * | 2021-03-15 | 2021-06-15 | 中国美术学院 | Product and matching supply relation visualization method and system thereof |
| WO2022268118A1 (en) * | 2021-06-22 | 2022-12-29 | 北京锐安科技有限公司 | Adjustment method and apparatus for relationship diagram, and storage medium |
| CN114036700A (en) * | 2021-10-27 | 2022-02-11 | 中南大学 | Layout method of network asset graph |
| CN116502716A (en) * | 2023-06-27 | 2023-07-28 | 深圳大学 | Knowledge graph layout method, device, equipment and medium |
| CN116502716B (en) * | 2023-06-27 | 2023-09-26 | 深圳大学 | Knowledge graph layout method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111897973A (en) | WebGL-based mass node knowledge graph visual layout method and system | |
| US7663621B1 (en) | Cylindrical wrapping using shader hardware | |
| EP1594091B1 (en) | System and method for providing an enhanced graphics pipeline | |
| US7978205B1 (en) | Systems and methods for providing an enhanced graphics pipeline | |
| JP6713036B2 (en) | Method and apparatus for performing a convolution operation on folded feature data | |
| CN103150699B (en) | Graphics command generating apparatus and graphics command generation method | |
| CN105809728A (en) | Rendering views of scene in a graphics processing unit | |
| JP2004348702A (en) | Image processing method, its apparatus, and its processing system | |
| CN109237999B (en) | Method and system for drawing batch three-dimensional situation target trail in real time | |
| US8212816B1 (en) | System, method, and computer program product for parallel ray tracing traversal and intersection utilizing a single instruction multiple data (SIMD) processing architecture | |
| JP2013539095A (en) | Split storage of anti-aliased samples | |
| JP3892016B2 (en) | Image processing apparatus and image processing method | |
| CN113077531A (en) | Tiling primitives in a graphics processing system | |
| CN116245997A (en) | Three-dimensional model dynamic rendering parallel acceleration method and system based on supercomputer | |
| US20240062453A1 (en) | Generation of tight world space bounding regions | |
| JP2010500634A (en) | Method for coding a digital mockup of an object in the form of a composite image and a system for display on a screen | |
| Yuan et al. | Real-time Simulation for 3D Tissue Deformation with CUDA Based GPU Computing. | |
| Amador et al. | A CUDA-based implementation of stable fluids in 3D with internal and moving boundaries | |
| US20040181373A1 (en) | Visual simulation of dynamic moving bodies | |
| Gissler et al. | Efficient Uniform Grids for Collision Handling in Medical Simulators. | |
| Yin et al. | Multi-screen Tiled Displayed, Parallel Rendering System for a Large Terrain Dataset. | |
| CN111383312A (en) | Large-scale knowledge graph visualization method and device based on particle system | |
| Krishnasamy et al. | Multi-gpu implementations of parallel 3d sweeping algorithms with application to geological folding | |
| CN116152405B (en) | Service processing method and device, computer equipment and storage medium | |
| Niebling et al. | D5. 3.1–Remote hybrid rendering: analysis and system definition for exascale systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201106 |