One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause an electronic device to perform operations, the operations comprising:
- receiving a graph representative of a domain, and a label associated with each node of a set of nodes of the received graph;
- classifying each node of the set of nodes into a classification based on the label associated with each node of the set of nodes;
- generating an explanation providing a reason for the classification;
- extracting a set of first sub-graphs from the received graph;
- reducing each first sub-graph of the extracted set of first sub-graphs to obtain a set of reduced sub-graphs corresponding to each first sub-graph of the extracted set of first sub-graphs;
- executing a first set of operations to obtain a set of second sub-graphs from the extracted set of first sub-graphs, based on the reduction of each first sub-graph of the extracted set of first sub-graphs, wherein the first set of operations includes:
- determining a closest reduced sub-graph, from the set of reduced sub-graphs, corresponding to each first sub-graph of the extracted set of first sub-graphs,
- determining a set of coverage metrics based on the extracted set of first sub-graphs and the determined closest reduced sub-graph corresponding to each first sub-graph of the extracted set of first sub-graphs,
- determining whether the determined set of coverage metrics satisfy a set of coverage conditions, and
- re-iterating the reduction of the extracted set of first sub-graphs based on the determination that the determined set of coverage metrics does not satisfy the set of coverage conditions;
- obtaining the set of second sub-graphs from the determined closest reduced sub-graph corresponding to each first sub-graph of the extracted set of first sub-graphs, based on an iterative control of the execution of the first set of operations until the determined set of coverage metrics satisfy the set of coverage conditions;
- training a graph machine learning model based on the obtained set of second sub-graphs and the received label associated with each node of the set of nodes of the received graph;
- obtaining a first list of extract-nodes and a first list of non-extract-nodes in the determined closest reduced sub-graph corresponding to each first sub-graph of the extracted set of first sub-graphs;
- obtaining a second list of extract-nodes and a second list of non-extract-nodes based on the extracted set of first sub-graphs;
- determining a first distribution of node repetition and a first distribution of node degree associated with the first list of extract-nodes;
- determining a second distribution of node repetition associated with the first list of non-extract-nodes;
- determining a second distribution of node degree associated with the second list of extract-nodes;
- and
- determining a third distribution of node repetition associated with the second list of non-extract-nodes,
- the set of coverage metrics is determined based on at least one of:
- the first distribution of node repetition,
- the first distribution of node degree,
- the second distribution of node repetition,
- the second distribution of node degree, or
- the third distribution of node repetition;
- training an explainable prediction model based on the graph machine learning model and the determined closest reduced sub-graph corresponding to each first sub-graph of the extracted set of first sub-graphs.