PREDICTING PRIORITY OF SITUATIONS

1 . A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:

input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios and historical ticket data, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; and

process, by the neural network model, the situation event graph and the corresponding scenario to determine a priority of the situation.

2 . The computer program product of claim 1 , wherein the situation event graph and corresponding scenario include topology data and knowledge graph data.

3 . The computer program product of claim 1 , wherein the priority of the situation includes a status indicator for the situation.

4 . The computer program product of claim 3 , wherein the instructions are further configured to cause the at least one computing device to:

generate and output a visualization to a user interface, the visualization indicating the status indicator for the situation.

5 . The computer program product of claim 4 , wherein the instructions are further configured to cause the at least one computing device to:

order the situation in relation to other situations using the priority.

6 . The computer program product of claim 5 , wherein the instructions are further configured to cause the at least one computing device to:

input a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation;

process, by the neural network model, the new situation event graph and the corresponding new scenario to determine a new priority of the new situation; and

output and reorder the visualization to include the new situation and the new priority.

7 . The computer program product of claim 1 , wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate.

8 . The computer program product of claim 1 , wherein the neural network model comprises a graph neural network (GNN) model.

9 . A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:

input a situation event graph, topology data associated with the situation event graph, a knowledge graph associated with the situation event graph, and historical ticket data into a neural network model;

generate a predicted priority for the situation event graph using the neural network model;

compare the predicted priority to an actual priority for the situation event graph to determine a loss; and

input the loss as feedback to the neural network model to improve the neural network model.

10 . The computer program product of claim 9 , wherein the instructions are further configured to cause the at least one computing device to:

determine the actual priority for the situation using the historical ticket data.

11 . The computer program product of claim 9 , wherein the predicted priority includes a status indicator.

12 . The computer program product of claim 9 , wherein the actual priority includes an actual event and an actual priority of the event.

13 . The computer program product of claim 9 , wherein the neural network model includes a graph neural network model.

14 . A computer-implemented method, the computer-implemented method further comprising:

inputting a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios and historical ticket data, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; and

processing, by the neural network model, the situation event graph and the corresponding scenario to determine a priority of the situation.

15 . The computer-implemented method of claim 14 , wherein the situation event graph and corresponding scenario include topology data and knowledge graph data.

16 . The computer-implemented method of claim 14 , wherein the priority of the situation includes a status indicator for the situation.

17 . The computer-implemented method of claim 16 , further comprising:

generating and outputting a visualization to a user interface, the visualization indicating the status indicator for the situation.

18 . The computer-implemented method of claim 17 , further comprising:

ordering the situation in relation to other situations using the priority.

19 . The computer-implemented method of claim 18 , further comprising:

inputting a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation;

processing, by the neural network model, the new situation event graph and the corresponding new scenario to determine a new priority of the new situation; and

outputting and reordering the visualization to include the new situation and the new priority.

20 . The computer-implemented method of claim 14 , wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate.

21 . The computer-implemented method of claim 14 , wherein the neural network model comprises a graph neural network (GNN) model.