SYSTEM, METHOD, DEVICE, AND PROGRAM FOR ENHANCED GRAPH-BASED NODE CLASSIFICATION

1 . A method for graph embedding, the method being executed by at least one processor, the method comprising:

receiving graph data associated with one or more users;

receiving classification data associated with the one or more users, wherein the classification data comprises non-graph data associated with the one or more users;

generating an accuracy parameter based on the classification data associated with the one or more users, wherein the accuracy parameter indicates an accuracy of neural network-based classification results based on the classification data; and

generating numerical node representation using a neural network-based graph embedding model associated with the one or more users based on the graph data, the classification data, and the accuracy parameter.

2 . The method of claim 1 , wherein the method further comprises:

inputting the numerical node representation and a concatenation of node attributes into a neural network-based classifier, wherein the neural network-based classifier is trained using the classification data associated with the one or more users; and

generating classification results based on the neural network-based classifier.

3 . The method of claim 1 , wherein the neural network-based graph embedding model is based on a combination of a first function based on user relationships based on the graph data and a second function based on user classification labels based on the classification data.

4 . The method of claim 1 , wherein the numerical node representation of a first user among the one or more users and a second user among the one or more users have a cosine similarity higher than a threshold, and wherein the first user and the second user have a same classification label based on the classification data.

5 . The method of claim 1 , wherein the numerical node representation of a first user among the one or more users and a second user among the one or more users have a cosine similarity higher than a threshold, and wherein the first user and the second user are in a same neighborhood based on the graph data.

6 . The method of claim 3 , wherein generating the numerical node representation using the neural network-based graph embedding model comprises minimizing the combination of the first function and the second function.

7 . The method of claim 3 , wherein one or more second layers of the neural network-based graph embedding model process the combination of the first function and the second function.

8 . The method of claim 1 , wherein the accuracy parameter is based on a concatenation of one or more user attributes, wherein the one or more user attributes are based on the non-graph data.

9 . The method of claim 1 , wherein one or more first layers of the neural network-based graph embedding model process one or more user attributes based on the non-graph data.

10 . An apparatus for graph embedding, the apparatus comprising:

at least one memory configured to store program code; and

at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:

first receiving code configured to cause the at least one processor to receive graph data associated with one or more users;

second receiving code configured to cause the at least one processor to receive classification data associated with the one or more users, wherein the classification data comprises non-graph data associated with the one or more users;

first generating code configured to cause the at least one processor to generate an accuracy parameter based on the classification data associated with the one or more users, wherein the accuracy parameter indicates an accuracy of neural network-based classification results based on the classification data; and

second generating code configured to cause the at least one processor to generate numerical node representation using a neural network-based graph embedding model associated with the one or more users based on the graph data, the classification data, and the accuracy parameter.

11 . The apparatus of claim 10 , wherein the program code further comprises:

inputting code configured to cause the at least one processor to input the numerical node representation and a concatenation of node attributes into a neural network-based classifier, wherein the neural network-based classifier is trained using the classification data associated with the one or more users; and

third generating code configured to cause the at least one processor to generate classification results based on the neural network-based classifier.

12 . The apparatus of claim 10 , wherein the neural network-based graph embedding model is based on a combination of a first function based on user relationships based on the graph data and a second function based on user classification labels based on the classification data.

13 . The apparatus of claim 10 , wherein the numerical node representation of a first user among the one or more users and a second user among the one or more users have a cosine similarity higher than a threshold, and wherein the first user and the second user have a same classification label based on the classification data.

14 . The apparatus of claim 10 , wherein the numerical node representation of a first user among the one or more users and a second user among the one or more users have a cosine similarity higher than a threshold, and wherein the first user and the second user are in a same neighborhood based on the graph data.

15 . The apparatus of claim 12 , wherein generating the numerical node representation using the neural network-based graph embedding model comprises minimizing the combination of the first function and the second function.

16 . The apparatus of claim 12 , wherein one or more second layers of the neural network-based graph embedding model process the combination of the first function and the second function.

17 . The apparatus of claim 10 , wherein the accuracy parameter is based on a concatenation of one or more user attributes, wherein the one or more user attributes are based on the non-graph data.

18 . A non-transitory computer-readable medium storing instructions for graph embedding, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to:

receive graph data associated with one or more users;

receive classification data associated with the one or more users, wherein the classification data comprises non-graph data associated with the one or more users;

generate an accuracy parameter based on the classification data associated with the one or more users, wherein the accuracy parameter indicates an accuracy of neural network-based classification results based on the classification data; and

generate numerical node representation using a neural network-based graph embedding model associated with the one or more users based on the graph data, the classification data, and the accuracy parameter.

19 . The non-transitory computer-readable medium of claim 18 , wherein the neural network-based graph embedding model is based on a combination of a first function based on user relationships based on the graph data and a second function based on user classification labels based on the classification data.

20 . The non-transitory computer-readable medium of claim 19 , wherein generating the numerical node representation using the neural network-based graph embedding model comprises minimizing the combination of the first function and the second function.