CN109743286A - A kind of IP type mark method and apparatus based on figure convolutional neural networks - Google Patents
A kind of IP type mark method and apparatus based on figure convolutional neural networks Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of IP type mark method and apparatus based on figure convolutional neural networks, wherein, provided method includes: that the whole network data obtained in preset time period establishes adjacency matrix and eigenmatrix according to the structural information in the whole network data；The adjacency matrix and the eigenmatrix are input in preset figure convolutional neural networks, the classification results of each IP in the whole network data are obtained.Method provided in an embodiment of the present invention, by constructing structural information to whole network data, characteristic information in figure is extracted with structural information, classified using figure convolutional neural networks to the IP in the whole network information, the IP segment table provided without operator carries out data supporting, and classified using whole network data, feedback can be provided extremely IP in time, improves internet security.
Description
Technical field
The present embodiments relate to technical field of network security more particularly to a kind of IP classes based on figure convolutional neural networks
Phenotypic marker method and apparatus.
Background technique
With the rapid development of network technology and the arrival of cybertimes, the wide and abundant resource that network is contained,
Many conveniences are brought to human society.However, just while people's lives are increasingly dependent on network, by interests driving
The network safety event of generation but emerges one after another, and Internet service security fields are often faced black produce and carried out using robot
Illegal business operation.
In the prior art, usually using the mode of IP portrait, to distinguish the authenticity of access user, however, existing IP
Portrait means need to be classified based on the macrocyclic historical act of IP and the IP segment table that provides in conjunction with operator current to infer
The type of the IP, it is limited to have ignored IP resource, there is the case where being re-used, and especially in IP resource, abundant and floating resources are not
In the case where abundance；Can exist based on historical analysis result inaccuracy, operator's IP list update not in time the case where, in reality
The IP result for continuing to continue to use IP portrait offer in these upstreams in business can cause serious shadow to business in specific network strategy
Pilot causes user experience bad, while network security is unable to get effective guarantee.
Summary of the invention
The embodiment of the present invention provides a kind of IP type mark method and system based on figure convolutional neural networks, to solve
In the prior art to IP classification depend on operator IP list, when IP list update not in time in the case where, specific
Can cause to seriously affect to business in network strategy causes user experience bad, while network security is unable to get effective guarantee
The problem of.
In a first aspect, the embodiment of the present invention provides a kind of IP type mark method based on figure convolutional neural networks, comprising:
The whole network data obtained in preset time period establishes adjacency matrix according to the structural information in the whole network data
And eigenmatrix；
The adjacency matrix and the eigenmatrix are input in preset figure convolutional neural networks, described the whole network is obtained
The classification results of each IP in data.
Second aspect, the embodiment of the present invention provide a kind of IP type mark system based on figure convolutional neural networks, comprising:
Feature construction module, for obtaining the whole network data in preset time period, according to the structure in the whole network data
Information establishes adjacency matrix and eigenmatrix；
Categorization module, for the adjacency matrix and the eigenmatrix to be input to preset figure convolutional neural networks
In, obtain the classification results of each IP in the whole network data.
The third aspect, the embodiment of the present invention provides a kind of electronic equipment, including memory, processor and is stored in memory
Computer program that is upper and can running on a processor, the processor are realized when executing described program such as above-mentioned first aspect institute
The step of IP type mark method based on figure convolutional neural networks provided.
Fourth aspect, the embodiment of the present invention provide a kind of non-transient computer readable storage medium, are stored thereon with calculating
Machine program is realized when the computer program is executed by processor and is based on figure convolutional neural networks as provided by above-mentioned first aspect
IP type mark method the step of.
IP type mark method and system provided in an embodiment of the present invention based on figure convolutional neural networks, by the whole network
Data construct structural information, extract to the characteristic information in figure with structural information, using figure convolutional neural networks to the whole network
IP in information classifies, and the IP segment table provided without operator carries out data supporting, and is classified using whole network data,
Feedback can be provided extremely to IP in time, improve internet security.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the process signal for the IP type mark method based on figure convolutional neural networks that one embodiment of the invention provides
Figure；
Structural information in the IP type mark method based on figure convolutional neural networks that Fig. 2 provides for one embodiment of the invention
Local exemplary diagram；
Picture scroll product mind in the IP type mark method based on figure convolutional neural networks that Fig. 3 provides for one embodiment of the invention
Input schematic diagram through network；
Fig. 4 is the structural representation for the IP type mark system based on figure convolutional neural networks that one embodiment of the invention provides
Figure；
Fig. 5 is the structural schematic diagram for the electronic equipment that one embodiment of the invention provides.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
With reference to Fig. 1, Fig. 1 is the IP type mark method based on figure convolutional neural networks that one embodiment of the invention provides
Flow diagram, provided method include:
S1 obtains the whole network data in preset time period, according to the structural information in the whole network data, establishes adjacent square
Battle array and eigenmatrix；
Specifically, by obtaining whole network data in a period of time, since whole network data is a dynamic data,
Therefore whole network data all in a period can be extracted by the way of time series, whole network data is constituted by saving
Structural information of the graph data of point and side composition as whole network data, refering to what is shown in Fig. 2, wherein, the node of structural information can
Think the network nodes such as IP, DeviceID, UA, Referer, information exchange and information verification process conduct between different nodes
It side in structural information further can be by information sender to the side as side between structural information interior joint and node
To.After obtaining the structural information of whole network data, information further obtains adjacency matrix and eigenmatrix with this configuration,
With all vertex datas in an one-dimension array storage figure；The number of relationship (side or arc) between vertex is stored with a two-dimensional array
According to this two-dimensional array is known as adjacency matrix.Adjacency matrix is divided into digraph adjacency matrix and non-directed graph adjacency matrix again.For
The characteristic for receiving the output of the node and each node in information may be constructed eigenmatrix, adjacency matrix and eigenmatrix
The as input data of figure convolutional neural networks.
The adjacency matrix and the eigenmatrix are input in preset figure convolutional neural networks, described in acquisition by S2
The classification results of each IP in whole network data.
Specifically, the adjacency matrix of building and eigenmatrix are input in preset figure convolutional neural networks, picture scroll product
Neural network can export by forward direction from level to level and provide probability results in the last layer, and then provide each in whole network data
The concrete type result of a IP.
By the method, by constructing structural information to whole network data, the characteristic information in figure is carried out with structural information
It extracts, is classified using figure convolutional neural networks to the IP in the whole network information, the IP segment table provided without operator is counted
According to support, and classified using whole network data, feedback can be provided extremely IP in time, improves internet security.
On the basis of the above embodiments, the step of whole network data obtained in preset time period, specifically includes: with
Each of network verifies node as structural information of event and attribute relevant to the verification time, between node
Data interaction as in structural information side building whole network data structural information.
Specifically, in the step of passing through the whole network acquisition of information structural information, it is first determined the node in structural information leads to
Verify data is crossed to obtain structural information, node is verifying event and event relevant attribute node such as IP, DeviceID,
The nodes such as UA, the information interactive process in verification information can be obtained as the side in structural information by whole network data structure
The full mesh topology figure built, the structure of as whole network data are new.
By the method, whole network data is subjected to image conversion, and then the whole network number can be obtained on the basis of structural information
According to topological diagram in figure characteristic information and structural information, convenient for it is subsequent using picture scroll product to each node in structural information into
Row classification.
On the basis of the above embodiments, described that the adjacency matrix and the eigenmatrix are input to preset picture scroll
The step of accumulating in neural network, obtaining the classification results of each IP in the whole network data specifically includes: will be according to the whole network number
The adjacency matrix and eigenmatrix that structural information in is established are input in preset figure convolutional neural networks, by preset
Figure convolutional neural networks are labeled each of structural information node；Choose IP all in the whole network data
Node extracts the annotation results of each IP node, obtains the classification results of each IP in the whole network data.
Specifically, Fig. 3 is the IP type mark based on figure convolutional neural networks that one embodiment of the invention provides with reference to Fig. 3
The input schematic diagram of figure convolutional neural networks in note method.By being input to the eigenmatrix constructed in S1 and adjacency matrix
In preset figure convolutional neural networks, figure convolutional neural networks can be obtained to the qualitative table of each node in structural information
It counts, is exactly the type that meter goes out IP for IP node, by extracting the annotation results of each of whole network data IP node,
And then the classification results of IP in whole network data can be obtained.
On the basis of the above embodiments, described the step of obtaining the classification results of each IP in the whole network data it
Afterwards, further includes: according to the classification results of the IP, calculate the confidence level that each IP is exception IP, confidence level is higher than default
The classification results of the IP of threshold value are as final IP classification results.
Specifically, since classification results of the figure convolutional neural networks for IP are the probability that IP is normal IP or exception IP
Output, since a part of normal IP is when being classified, will lead to some behaviors, to make the IP have certain probability to regard as different
Normal IP, therefore the concept of confidence level is incorporated herein, in the case that the confidence level that an IP is exception IP is higher than preset threshold,
It can determine the IP really for abnormal IP, reduce the misjudged probability of ordinary user, promote user's body of the user in verification process
It tests.
On the basis of the above embodiments, before the whole network data obtained in preset time period the step of, further includes:
The whole network data of multiple and different preset time periods is obtained, and each of whole network data node is labeled, is constructed
Training sample set；Figure convolutional neural networks are trained by the training sample set, obtain the preset picture scroll product mind
Through network.
Specifically, being carried out by the whole network data for obtaining multiple and different periods, and to node each in whole network data
Data after mark are trained figure convolutional neural networks as training sample set, can obtain default in the present embodiment
Figure convolutional neural networks.
By the method, without the IP portrait that third party provides, but the whole network data in network is labeled
It is trained, to obtain IP type identification model, the user experience is improved is differentiated to subsequent IP type.
With reference to Fig. 4, Fig. 4 is the IP type mark system based on figure convolutional neural networks that one embodiment of the invention provides
Structural schematic diagram, provided system include: feature construction module 41 and categorization module 42.
Wherein, feature construction module 41 is used to obtain the whole network data in preset time period, according in the whole network data
Structural information, establish adjacency matrix and eigenmatrix.
Categorization module 42 is used to the adjacency matrix and the eigenmatrix being input to preset figure convolutional neural networks
In, obtain the classification results of each IP in the whole network data.
Specifically, by obtaining whole network data in a period of time, since whole network data is a dynamic data,
Therefore whole network data all in a period can be extracted by the way of time series, whole network data is constituted by saving
Structural information of the graph data of point and side composition as whole network data, refering to what is shown in Fig. 2, wherein, the node of structural information can
Think the network nodes such as IP, DeviceID, UA, Referer, information exchange and information verification process conduct between different nodes
It side in structural information further can be by information sender to the side as side between structural information interior joint and node
To.After obtaining the structural information of whole network data, information further obtains adjacency matrix and eigenmatrix with this configuration,
With all vertex datas in an one-dimension array storage figure；The number of relationship (side or arc) between vertex is stored with a two-dimensional array
According to this two-dimensional array is known as adjacency matrix.Adjacency matrix is divided into digraph adjacency matrix and non-directed graph adjacency matrix again.For
The characteristic for receiving the output of the node and each node in information may be constructed eigenmatrix, adjacency matrix and eigenmatrix
The as input data of figure convolutional neural networks.
The adjacency matrix of building and eigenmatrix are input in preset figure convolutional neural networks, figure convolutional neural networks
It can be exported by forward direction from level to level and provide probability results in the last layer, and then provide the tool of each IP in whole network data
Body types results.
By this system, by constructing structural information to whole network data, the characteristic information in figure is carried out with structural information
It extracts, is classified using figure convolutional neural networks to the IP in the whole network information, the IP segment table provided without operator is counted
According to support, and classified using whole network data, feedback can be provided extremely IP in time, improves internet security.
On the basis of the above embodiments, the system also includes confidence level modules, for the classification knot according to the IP
Fruit calculates the confidence level that each IP is exception IP, and confidence level is higher than the classification results of the IP of preset threshold as final IP
Classification results.
Specifically, since classification results of the figure convolutional neural networks for IP are the probability that IP is normal IP or exception IP
Output, since a part of normal IP is when being classified, will lead to some behaviors, to make the IP have certain probability to regard as different
Normal IP, therefore the concept of confidence level is incorporated herein, in the case that the confidence level that an IP is exception IP is higher than preset threshold,
It can determine the IP really for abnormal IP, reduce the misjudged probability of ordinary user, promote user's body of the user in verification process
It tests.
On the basis of the above embodiments, the system also includes training modules, for obtaining multiple and different preset times
The whole network data of section, and each of whole network data node is labeled, construct training sample set；Pass through the instruction
Practice sample set to be trained the preset figure convolutional neural networks.
By obtaining the whole network data of multiple and different periods, and after being labeled to node each in whole network data
Data are trained figure convolutional neural networks as training sample set, can obtain the preset picture scroll product in the present embodiment
Neural network.
By this system, without the IP portrait that third party provides, but the whole network data in network is labeled
It is trained, to obtain IP type identification model, the user experience is improved is differentiated to subsequent IP type.
Fig. 5 is the structural schematic diagram for the electronic equipment that one embodiment of the invention provides, as shown in figure 5, provided electronics
Equipment includes: processor (processor) 501,502, memory communication interface (Communications Interface)
(memory) 503 and bus 504, wherein processor 501, communication interface 502, memory 503 are completed mutually by bus 504
Between communication.Processor 501 can call the logical order in memory 503, to execute following method, for example, obtain
Whole network data in preset time period establishes adjacency matrix and eigenmatrix according to the structural information in the whole network data；It will
The adjacency matrix and the eigenmatrix are input in preset figure convolutional neural networks, are obtained each in the whole network data
The classification results of a IP.
The embodiment of the present invention discloses a kind of computer program product, and computer program product includes being stored in non-transient calculating
Computer program on machine readable storage medium storing program for executing, computer program include program instruction, when program instruction is computer-executed,
Computer is able to carry out method provided by above-mentioned each method embodiment, for example, obtains the whole network number in preset time period
According to establishing adjacency matrix and eigenmatrix according to the structural information in the whole network data；By the adjacency matrix and the spy
Input matrix is levied into preset figure convolutional neural networks, obtains the classification results of each IP in the whole network data.
The present embodiment provides a kind of non-transient computer readable storage medium, non-transient computer readable storage medium storages
Computer instruction, computer instruction make computer execute method provided by above-mentioned each method embodiment, for example, obtain pre-
If the whole network data in the period establishes adjacency matrix and eigenmatrix according to the structural information in the whole network data；By institute
It states adjacency matrix and the eigenmatrix is input in preset figure convolutional neural networks, obtain each in the whole network data
The classification results of IP.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of IP type mark method based on figure convolutional neural networks characterized by comprising
The whole network data obtained in preset time period establishes adjacency matrix and spy according to the structural information in the whole network data
Levy matrix；
The adjacency matrix and the eigenmatrix are input in preset figure convolutional neural networks, the whole network data is obtained
In each IP classification results.
2. the method according to claim 1, wherein the step for obtaining the whole network data in preset time period
Suddenly, it specifically includes:
Event and attribute relevant to the verification time are verified as the node of structural information, section using each of network
Structural information of the data interaction as the side building whole network data in structural information between point.
3. the method according to claim 1, wherein described input the adjacency matrix and the eigenmatrix
Into preset figure convolutional neural networks, the step of obtaining the classification results of each IP in the whole network data, is specifically included:
The adjacency matrix and eigenmatrix established according to the structural information in whole network data are input to preset figure convolutional Neural
In network, each of structural information node is labeled by preset figure convolutional neural networks；
IP node all in the whole network data is chosen, the annotation results of each IP node are extracted, obtains the whole network number
The classification results of each IP in.
4. the method according to claim 1, wherein the classification for obtaining each IP in the whole network data
As a result after the step of, further includes:
According to the classification results of the IP, the confidence level that each IP is exception IP is calculated, confidence level is higher than preset threshold
The classification results of IP are as final IP classification results.
5. the method according to claim 1, wherein before the whole network data obtained in preset time period
Step, further includes:
The whole network data of multiple and different preset time periods is obtained, and each of whole network data node is labeled,
Construct training sample set；
Figure convolutional neural networks are trained by the training sample set, obtain the preset figure convolutional neural networks.
6. a kind of IP type mark system based on figure convolutional neural networks characterized by comprising
Feature construction module, for obtaining the whole network data in preset time period, according to the structural information in the whole network data,
Establish adjacency matrix and eigenmatrix；
Categorization module is obtained for the adjacency matrix and the eigenmatrix to be input in preset figure convolutional neural networks
Obtain the classification results of each IP in the whole network data.
7. system according to claim 6, which is characterized in that the system also includes:
Confidence level module calculates the confidence level that each IP is exception IP, by confidence level for the classification results according to the IP
Classification results higher than the IP of preset threshold are as final IP classification results.
8. system according to claim 6, which is characterized in that the system also includes:
Training module, for obtaining the whole network data of multiple and different preset time periods, and by each of described whole network data
Node is labeled, and constructs training sample set；
The preset figure convolutional neural networks are trained by the training sample set.
9. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor is realized as described in any one of claim 1 to 5 when executing described program based on figure
The step of IP type mark method of convolutional neural networks.
10. a kind of non-transient computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer
It realizes as described in any one of claim 1 to 5 when program is executed by processor based on the IP type mark of figure convolutional neural networks
The step of method.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110390259A (en) * | 2019-06-11 | 2019-10-29 | 中国科学院自动化研究所南京人工智能芯片创新研究院 | Recognition methods, device, computer equipment and the storage medium of diagram data |
| CN111669373A (en) * | 2020-05-25 | 2020-09-15 | 山东理工大学 | Network anomaly detection method and system based on space-time convolutional network and topology perception |
| WO2021031825A1 (en) * | 2019-08-22 | 2021-02-25 | 深圳壹账通智能科技有限公司 | Network fraud identification method and device, computer device, and storage medium |
| CN113288131A (en) * | 2021-05-06 | 2021-08-24 | 广东工业大学 | Non-invasive blood glucose detection method, processor and device based on graph convolution network |
| CN113411841A (en) * | 2020-03-17 | 2021-09-17 | 中国移动通信集团浙江有限公司 | 5G slice cutting and joining method and device and computing equipment |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101674336A (en) * | 2008-09-08 | 2010-03-17 | 大唐移动通信设备有限公司 | Method, system and device for allocating IP addresses |
| CN103812961A (en) * | 2013-11-01 | 2014-05-21 | 北京奇虎科技有限公司 | Method and device for recognizing Internet protocol (IP) addresses of designated class and defending method and system |
| CN104602142A (en) * | 2015-01-29 | 2015-05-06 | 太仓市同维电子有限公司 | Business classification method based on neutral network learning |
| EP2612481B1 (en) * | 2010-09-03 | 2015-10-21 | Telefónica, S.A. | Method and system for classifying traffic |
| CN105376247A (en) * | 2015-11-30 | 2016-03-02 | 睿峰网云（北京）科技股份有限公司 | Method and device for identifying abnormal flow based on frequent algorithm |
| CN105721406A (en) * | 2014-12-05 | 2016-06-29 | 中国移动通信集团广东有限公司 | Method and device for obtaining IP black list |
| US20170061276A1 (en) * | 2015-09-01 | 2017-03-02 | Google Inc. | Neural network for processing graph data |
| KR20170099238A (en) * | 2016-02-23 | 2017-08-31 | 한국전자통신연구원 | Load control apparatus and method in mobile system |
| CN108173704A (en) * | 2017-11-24 | 2018-06-15 | 中国科学院声学研究所 | A kind of method and device of the net flow assorted based on representative learning |
| CN108648095A (en) * | 2018-05-10 | 2018-10-12 | 浙江工业大学 | A kind of nodal information hidden method accumulating gradient network based on picture scroll |
| CN108647263A (en) * | 2018-04-28 | 2018-10-12 | 淮阴工学院 | A kind of network address method for evaluating confidence crawled based on segmenting web page |
| CN108874914A (en) * | 2018-05-29 | 2018-11-23 | 吉林大学 | A kind of information recommendation method based on the long-pending and neural collaborative filtering of picture scroll |
-
2018
- 2018-11-29 CN CN201811443168.4A patent/CN109743286A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101674336A (en) * | 2008-09-08 | 2010-03-17 | 大唐移动通信设备有限公司 | Method, system and device for allocating IP addresses |
| EP2612481B1 (en) * | 2010-09-03 | 2015-10-21 | Telefónica, S.A. | Method and system for classifying traffic |
| CN103812961A (en) * | 2013-11-01 | 2014-05-21 | 北京奇虎科技有限公司 | Method and device for recognizing Internet protocol (IP) addresses of designated class and defending method and system |
| CN105721406A (en) * | 2014-12-05 | 2016-06-29 | 中国移动通信集团广东有限公司 | Method and device for obtaining IP black list |
| CN104602142A (en) * | 2015-01-29 | 2015-05-06 | 太仓市同维电子有限公司 | Business classification method based on neutral network learning |
| US20170061276A1 (en) * | 2015-09-01 | 2017-03-02 | Google Inc. | Neural network for processing graph data |
| CN105376247A (en) * | 2015-11-30 | 2016-03-02 | 睿峰网云（北京）科技股份有限公司 | Method and device for identifying abnormal flow based on frequent algorithm |
| KR20170099238A (en) * | 2016-02-23 | 2017-08-31 | 한국전자통신연구원 | Load control apparatus and method in mobile system |
| CN108173704A (en) * | 2017-11-24 | 2018-06-15 | 中国科学院声学研究所 | A kind of method and device of the net flow assorted based on representative learning |
| CN108647263A (en) * | 2018-04-28 | 2018-10-12 | 淮阴工学院 | A kind of network address method for evaluating confidence crawled based on segmenting web page |
| CN108648095A (en) * | 2018-05-10 | 2018-10-12 | 浙江工业大学 | A kind of nodal information hidden method accumulating gradient network based on picture scroll |
| CN108874914A (en) * | 2018-05-29 | 2018-11-23 | 吉林大学 | A kind of information recommendation method based on the long-pending and neural collaborative filtering of picture scroll |
Non-Patent Citations (4)
| Title |
|---|
| JAVY WANG: "图卷积网络详细介绍（一）", 《HTTPS://BLOG.CSDN.NET/DSTJWJW/ARTICLE/DETAILS/83896312》 * |
| ZHITANG CHEN, KE HE, JIAN LI AND YANHUI GENG: "Seq2Img: A Sequence-to-Image based Approach Towards IP Traffic Classification", 《2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIGDATA)》 * |
| 匿名: "图卷积网络定义和简单示例详解", 《WWW.ELECFANS.COM》 * |
| 齐金山，梁循, 李志宇,陈燕方, 许媛: "大规模复杂信息网络表示学习：概念、方法与挑战", 《计算机学报》 * |
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