| CPC H04L 63/1416 (2013.01) [G06F 16/9024 (2019.01); G06F 18/29 (2023.01); G06V 10/457 (2022.01); G06V 10/82 (2022.01); H04L 41/16 (2013.01); H04L 63/1425 (2013.01); H04L 63/1441 (2013.01)] | 10 Claims |

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1. A computer implemented method of feature detection in temporal graph data structures of events, the method comprising:
receiving a temporal series of graph data structures of events each including a plurality of nodes corresponding to events and edges connecting the nodes corresponding to relationships between events;
rendering each graph data structure in the temporal series as an image representation of the graph data structure including a representation of the nodes and the edges in the graph data structure being rendered reproducibly in a cartesian space based on attributes of the nodes and the edges, so as to generate a temporal series of image representations ordered according to the temporal graph data structures; and
processing the temporal series of image representations by a convolutional neural network to classify the temporal series of image representations so as to identify a feature in the temporal series of image representations, the convolutional neural network being trained by a supervised training method including a plurality of training example image series in which a subset of the plurality of training example image series are classified as including the feature.
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