CN114417958A - Unbalanced financial data credit evaluation method based on improved graph convolution neural network - Google Patents

Unbalanced financial data credit evaluation method based on improved graph convolution neural network Download PDF

Info

Publication number
CN114417958A
CN114417958A CN202111471951.3A CN202111471951A CN114417958A CN 114417958 A CN114417958 A CN 114417958A CN 202111471951 A CN202111471951 A CN 202111471951A CN 114417958 A CN114417958 A CN 114417958A
Authority
CN
China
Prior art keywords
graph
node
edge
neighborhood
training
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
Application number
CN202111471951.3A
Other languages
Chinese (zh)
Inventor
邱韵
徐小龙
邬晶
李少远
周松
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianyi Electronic Commerce Co Ltd
Original Assignee
Tianyi Electronic Commerce Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Tianyi Electronic Commerce Co Ltd filed Critical Tianyi Electronic Commerce Co Ltd
Priority to CN202111471951.3A priority Critical patent/CN114417958A/en
Publication of CN114417958A publication Critical patent/CN114417958A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/403—Solvency checks

Abstract

The invention discloses an unbalanced financial data credit evaluation method based on an improved graph convolution neural network, which comprises the following steps of: firstly, graph construction is carried out according to current financial feature data to obtain a graph G (V, E), wherein V is a point set, and E is an edge set. On the basis of the constructed graph, an improved graph convolution neural network is adopted to train a supervised learning model, and finally the trained model is used for predicting the credit of the financial user. The method relieves the over-fitting problem in the classified unbalanced financial data from the aspect of data enhancement; on the other hand, in the improved GCN model, each layer of graph convolution operation comprehensively utilizes information of node vectors and edge vectors in a first-order neighborhood, all graph node vectors are updated by weighting and aggregating the node vectors and the edge vectors, the effect of representing learning of the graph node vectors is improved from the model level, and the effect of carrying out classification evaluation on financial users with unbalanced categories is further improved.

Description

Unbalanced financial data credit evaluation method based on improved graph convolution neural network
Technical Field
The invention relates to the field of financial user credit evaluation, in particular to an unbalanced financial data credit evaluation method based on an improved graph convolution neural network.
Background
Data category imbalances are a common problem in the field of financial credit assessment. For example, in the case of fraud detection, the number of samples for fraud, breach, etc. is much smaller than the normal number of samples, because on the one hand the proportion of bad users among the total users is relatively small, and on the other hand users with fraud may conceal or falsify their fraud records. This creates a severe imbalance in the distribution of the number of samples in the minority class (rogue users) versus the number of samples in the majority class (normal users). When the traditional machine learning model learns the category unbalanced data, a good generalization effect is often obtained in most types of samples, and for few types of samples, due to the fact that the training set is small in scale, severe overfitting may occur, and generalization performance is poor. In a training set with a small number of samples, the number of samples of a few classes is further limited, and even a problem that some samples of a few classes are missing in the training set, namely "class missing", may occur. Therefore, how to solve the problems that the model generalization performance is poor and the learning of a few samples is difficult to be effectively realized due to the unbalanced class distribution in the financial data is another challenge in the field of financial credit evaluation.
The existing method for solving the problem of data category imbalance mainly comprises resampling, data synthesis, reweighting, transfer learning, meta learning, metric learning and the like. Wherein resampling may exacerbate overfitting of the model to a few classes of data; data synthesis may introduce noise or features that are not useful for classification, reducing classifier performance; metric learning is based on the distance between sample points, and an optimal decision boundary around a few types of samples is sought to be learned, but the method of measuring similarity by distance is often more limited under the condition of scarce labels; the migration learning and the meta learning both need to model a majority of samples and a minority of samples respectively, and when the number of samples and the number of categories are large, the complexity of the model is high. The method has some limitations when solving the problem of category imbalance in the financial data, so a simple and efficient algorithm framework is urgently needed to be provided for the limitation of the category imbalance in the financial data on the performance of the financial credit evaluation model.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide an unbalanced financial data credit evaluation method based on an improved graph convolutional neural network.
The invention provides the following technical scheme:
the invention provides an unbalanced financial data credit evaluation method based on an improved graph convolutional neural network, which comprises the following steps of:
s1, firstly, constructing a graph based on the financial characteristic data set; i.e. according to the input financial characteristic matrix X ∈ RN×D(N represents the total number of training sample sets, D is the dimension of the feature data), constructing a graph G (V, E) (V represents a node set, and E represents an edge set); each training sample corresponds to a node in the graph, the node is located in a D-dimensional Euclidean space, and each dimensional coordinate corresponds to the value of each dimensional feature of the sample; the graph construction mainly has two steps: firstly, determining a first-order neighborhood of each point by using a K nearest neighbor algorithm based on Euclidean distance, connecting a central node and all neighbor nodes by using edges, and then calculating the edge weight of each edge by using RBF mapping to further form a weighted adjacency matrix A of the whole graph, wherein the calculation formula of the edge weight is as follows:
Figure BDA0003392796340000021
where sigma represents the width parameter in the RBF function,
Figure BDA0003392796340000022
represents the square of the euclidean distance between nodes i and j; after RBF mapping, the weights of all edges are mapped between (0, 1), and edges between points closer in distance have larger weights;
s2, enhancing the training data by adopting a random graph enhancing method (as shown in FIG. 2); in the training data set, for a first-order neighborhood of each node, randomly eliminating nodes and corresponding edges in the neighborhood with a certain probability p; for any node v, its original first-order neighborhood can be represented as:
(u,e)∈N(v)
wherein u represents a node in a first-order neighborhood of the node v, and e represents an edge in the first-order neighborhood of the node v; after the random graph is enhanced, the first-order neighborhood of the node v is:
N(v)'＝N(v)-N(v)drop
wherein N (v)' is neighborhood after graph enhancement, N (v)dropIs a randomly deleted point set and an edge set in the neighborhood, and the scale ratio of the neighborhood before and after the graph enhancement satisfies | N (v) | (1-p) | N (v) |;
s3, training the improved GCN model by using the graph enhanced training set, wherein the updating rule (namely the spatial map convolution operation) of the layer-by-layer node representation vector of the improved GCN is defined as follows:
Figure BDA0003392796340000031
wherein the content of the first and second substances,
Figure BDA0003392796340000032
and
Figure BDA0003392796340000033
representing the representative vectors of the node v at the l-th level and the (l +1) -th level respectively,
Figure BDA0003392796340000034
and
Figure BDA0003392796340000035
the node in the first-order neighborhood N (v) of the node v respectively represents a vector and an edge represents a vector, and the initial representation vector can be randomly set; w(l)Represents the convolution kernel of the l layer, namely the weight matrix to be trained; f (-) represents an aggregation function of the node vectors and edge vectors in the neighborhood, where the direction is usedA quantity convolution operation function, i.e., f (a, b) ═ a × b; σ (-) represents the hidden layer activation function, and here adopts the RELU function as the hidden layer activation function;
for the update of the representation vector of each layer edge, a learnable matrix is simply introduced for training:
Figure BDA0003392796340000036
wherein the content of the first and second substances,
Figure BDA0003392796340000037
and
Figure BDA0003392796340000038
representing the representative vectors of the edge e at the l-th layer and the (l +1) -th layer respectively,
Figure BDA0003392796340000039
an edge update matrix representing the l-th layer;
s4, the financial credit evaluation task can be abstracted into a graph node classification task, so that the classification prediction result of the node can be obtained by the node expression vector only by adding softmax mapping to the output layer of the improved GCN, and the training of the final classifier is completed;
s5, training to obtain a final model, and testing in the test set to obtain a final financial credit classification prediction result; note that graph enhancement is only used during model training, and the original graph is still used as model input during testing.
Compared with the prior art, the invention has the following beneficial effects:
1. and enhancing the training data by adopting a random graph enhancing method. The overfitting problem in the category unbalanced financial data is effectively relieved from the aspect of data enhancement;
2. the classifier training is performed using an improved GCN model. In each layer of graph convolution operation, all graph node vectors are updated by weighting and aggregating node vectors and edge vectors in a first-order neighborhood, node information and edge information in the neighborhood are comprehensively utilized, the graph node vectors are improved from a model level to represent the learning effect, and further the effect of evaluating the credit of the unbalanced financial data is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a method for random map enhancement for enhancing training data;
FIG. 3 is a schematic diagram of an improved GCN model.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation. Wherein like reference numerals refer to like parts throughout.
Example 1
Referring to fig. 1-3, the present invention provides an unbalanced financial data credit evaluation method based on an improved graph convolutional neural network, comprising the following steps (the method flow chart is shown in fig. 1):
s1, a graph is first constructed based on the financial characteristic data set. I.e. according to the input financial characteristic matrix X ∈ RN×D(N represents the total number of training sample sets, D is the dimension of the feature data), and constructing a graph G (V, E) (V represents a node set, and E represents an edge set). Each training sample corresponds to a node in the graph, the nodes are located in the D-dimensional Euclidean space, and each dimensional coordinate corresponds to the value of each dimensional feature of the sample. The graph construction mainly has two steps: firstly, determining a first-order neighborhood of each point by using a K nearest neighbor algorithm based on Euclidean distance, connecting a central node and all neighbor nodes by using edges, and then calculating the edge weight of each edge by using RBF mapping to further form a weighted adjacency matrix A of the whole graph, wherein the calculation formula of the edge weight is as follows:
Figure BDA0003392796340000051
where sigma represents the width parameter in the RBF function,
Figure BDA0003392796340000052
representing the square of the euclidean distance between nodes i and j. After RBF mapping, the weights of all edges are mapped between (0, 1), and edges between points closer in distance have larger weights;
and S2, enhancing the training data by adopting a random graph enhancing method (as shown in FIG. 2). In the training data set, for a first-order neighborhood of each node, randomly eliminating nodes and corresponding edges in the neighborhood with a certain probability p. For any node v, its original first-order neighborhood can be represented as:
(u,e)∈N(v)
wherein u represents a node in the first-order neighborhood of node v, and e represents an edge in the first-order neighborhood of node v. After the random graph is enhanced, the first-order neighborhood of the node v is:
N(v)'＝N(v)-N(v)drop
wherein N (v)' is neighborhood after graph enhancement, N (v)dropIs a randomly deleted point set and an edge set in the neighborhood, and the scale ratio of the neighborhood before and after the graph enhancement satisfies | N (v) | (1-p) | N (v) |;
s3, training the improved GCN model by using the graph enhanced training set, wherein the updating rule (namely the spatial map convolution operation) of the layer-by-layer node representation vector of the improved GCN is defined as follows:
Figure BDA0003392796340000061
wherein the content of the first and second substances,
Figure BDA0003392796340000062
and
Figure BDA0003392796340000063
representing nodes v at the l-th and (l +1) -th layers, respectivelyThe vector is represented by a vector of values,
Figure BDA0003392796340000064
and
Figure BDA0003392796340000065
the initial representation vectors can be randomly arranged, and the node representation vectors and the edge representation vectors in the first-order neighborhood N (v) of the node v are respectively. W(l)Represents the convolution kernel of the l-th layer, i.e. the weight matrix to be trained. f (·, ·) represents an aggregation function for the intra-neighborhood node vectors and edge vectors, where a vector convolution operation is used, i.e., f (a, b) ═ a × b. σ (-) represents the hidden layer activation function, and here the RELU function is taken as the hidden layer activation function.
For the update of the representation vector of each layer edge, a learnable matrix is simply introduced for training:
Figure BDA0003392796340000066
wherein the content of the first and second substances,
Figure BDA0003392796340000067
and
Figure BDA0003392796340000068
representing the representative vectors of the edge e at the l-th layer and the (l +1) -th layer respectively,
Figure BDA0003392796340000069
an edge update matrix representing the l-th layer;
s4, the financial credit evaluation task can be abstracted into a graph node classification task, so that the classification prediction result of the node can be obtained by the node expression vector only by adding softmax mapping to the output layer of the improved GCN, and the training of the final classifier is completed (the model structure is shown in FIG. 3);
and S5, training to obtain a final model, and testing in the test set to obtain a final financial credit classification prediction result. Note that graph enhancement is only used during model training, and the original graph is still used as model input during testing.
The invention has the following advantages:
1. and enhancing the training data by adopting a random graph enhancing method. The overfitting problem in the category unbalanced financial data is effectively relieved from the aspect of data enhancement;
2. the classifier training is performed using an improved GCN model. In each layer of graph convolution operation, all graph node vectors are updated by weighting and aggregating node vectors and edge vectors in a first-order neighborhood, node information and edge information in the neighborhood are comprehensively utilized, the graph node vectors are improved from a model level to represent the learning effect, and further the effect of evaluating the credit of the unbalanced financial data is improved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (1)

1. The unbalanced financial data credit evaluation method based on the improved graph convolution neural network is characterized by comprising the following steps of:
s1, firstly, constructing a graph based on the financial characteristic data set; i.e. according to the input financial characteristic matrix X ∈ RN×D(N represents the total number of training sample sets, D is the dimension of the feature data), constructing a graph G (V, E) (V represents a node set, and E represents an edge set); each training sample corresponds to a node in the graph, the node is located in a D-dimensional Euclidean space, and each dimensional coordinate corresponds to the value of each dimensional feature of the sample; the graph construction mainly has two steps: firstly, determining a first-order neighborhood of each point by using a K nearest neighbor algorithm based on Euclidean distance, connecting a central node and all neighbor nodes by using edges, and then calculating each point by using RBF mappingThe edge weights of the edges further form a weighted adjacency matrix A of the whole graph, and the calculation formula of the edge weights is as follows:
Figure FDA0003392796330000011
where sigma represents the width parameter in the RBF function,
Figure FDA0003392796330000012
represents the square of the euclidean distance between nodes i and j; after RBF mapping, the weights of all edges are mapped between (0, 1), and edges between points closer in distance have larger weights;
s2, enhancing the training data by adopting a random graph enhancing method (as shown in FIG. 2); in the training data set, for a first-order neighborhood of each node, randomly eliminating nodes and corresponding edges in the neighborhood with a certain probability p; for any node v, its original first-order neighborhood can be represented as:
(u,e)∈N(v)
wherein u represents a node in a first-order neighborhood of the node v, and e represents an edge in the first-order neighborhood of the node v; after the random graph is enhanced, the first-order neighborhood of the node v is:
N(v)'＝N(v)-N(v)drop
wherein N (v)' is neighborhood after graph enhancement, N (v)dropIs a randomly deleted point set and an edge set in the neighborhood, and the scale ratio of the neighborhood before and after the graph enhancement satisfies | N (v) | (1-p) | N (v) |;
s3, training the improved GCN model by using the graph enhanced training set, wherein the updating rule (namely the spatial map convolution operation) of the layer-by-layer node representation vector of the improved GCN is defined as follows:
Figure FDA0003392796330000021
wherein the content of the first and second substances,
Figure FDA0003392796330000022
and
Figure FDA0003392796330000023
representing the representative vectors of the node v at the l-th level and the (l +1) -th level respectively,
Figure FDA0003392796330000024
and
Figure FDA0003392796330000025
the node in the first-order neighborhood N (v) of the node v respectively represents a vector and an edge represents a vector, and the initial representation vector can be randomly set; w(l)Represents the convolution kernel of the l layer, namely the weight matrix to be trained; f (·,) represents the aggregation function for the intra-neighborhood node vectors and edge vectors, where a vector convolution operation is used, i.e., f (a, b) ═ a × b; σ (-) represents the hidden layer activation function, and here adopts the RELU function as the hidden layer activation function;
for the update of the representation vector of each layer edge, a learnable matrix is simply introduced for training:
Figure FDA0003392796330000026
wherein the content of the first and second substances,
Figure FDA0003392796330000027
and
Figure FDA0003392796330000028
representing the representative vectors of the edge e at the l-th layer and the (l +1) -th layer respectively,
Figure FDA0003392796330000029
an edge update matrix representing the l-th layer;
s4, the financial credit evaluation task can be abstracted into a graph node classification task, so that the classification prediction result of the node can be obtained by the node expression vector only by adding softmax mapping to the output layer of the improved GCN, and the training of the final classifier is completed;
s5, training to obtain a final model, and testing in the test set to obtain a final financial credit classification prediction result; note that graph enhancement is only used during model training, and the original graph is still used as model input during testing.
CN202111471951.3A 2021-12-06 2021-12-06 Unbalanced financial data credit evaluation method based on improved graph convolution neural network Pending CN114417958A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111471951.3A CN114417958A (en) 2021-12-06 2021-12-06 Unbalanced financial data credit evaluation method based on improved graph convolution neural network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111471951.3A CN114417958A (en) 2021-12-06 2021-12-06 Unbalanced financial data credit evaluation method based on improved graph convolution neural network

Publications (1)

Publication Number Publication Date
CN114417958A true CN114417958A (en) 2022-04-29

Family

ID=81265376

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111471951.3A Pending CN114417958A (en) 2021-12-06 2021-12-06 Unbalanced financial data credit evaluation method based on improved graph convolution neural network

Country Status (1)

Country Link
CN (1) CN114417958A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114827021A (en) * 2022-06-27 2022-07-29 南京邮电大学 Multimedia service flow acceleration system based on SDN and machine learning

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114827021A (en) * 2022-06-27 2022-07-29 南京邮电大学 Multimedia service flow acceleration system based on SDN and machine learning

Similar Documents

Publication Publication Date Title
CN110533631B (en) SAR image change detection method based on pyramid pooling twin network
CN107103332A (en) A kind of Method Using Relevance Vector Machine sorting technique towards large-scale dataset
CN112685504B (en) Production process-oriented distributed migration chart learning method
CN107273387A (en) Towards higher-dimension and unbalanced data classify it is integrated
CN108985342A (en) A kind of uneven classification method based on depth enhancing study
CN113554100B (en) Web service classification method for enhancing attention network of special composition picture
CN110363230A (en) Stacking integrated sewage handling failure diagnostic method based on weighting base classifier
CN112700324A (en) User loan default prediction method based on combination of Catboost and restricted Boltzmann machine
CN112541639A (en) Recommendation system scoring prediction method based on graph neural network and attention mechanism
CN110705045A (en) Link prediction method for constructing weighting network by using network topological characteristics
CN113255844A (en) Recommendation method and system based on graph convolution neural network interaction
CN111367908A (en) Incremental intrusion detection method and system based on security assessment mechanism
CN114417958A (en) Unbalanced financial data credit evaluation method based on improved graph convolution neural network
CN113868537B (en) Recommendation method based on multi-behavior session graph fusion
CN117150416B (en) Method, system, medium and equipment for detecting abnormal nodes of industrial Internet
CN114708479A (en) Self-adaptive defense method based on graph structure and characteristics
CN111723010B (en) Software BUG classification method based on sparse cost matrix
CN117093928A (en) Self-adaptive graph node anomaly detection method based on spectral domain graph neural network
Jie et al. Social network group identification based on local attribute community detection
CN114265954B (en) Graph representation learning method based on position and structure information
CN112183820A (en) Linear programming based directed network link prediction method
KR20210003661A (en) Systems and methods for detecting flaws on panels using images of the panels
CN113159976B (en) Identification method for important users of microblog network
CN114519605A (en) Advertisement click fraud detection method, system, server and storage medium
CN111581297A (en) Symbolic network symbol prediction method based on graph convolution network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication