Abstract: A map fusing method includes receiving a source graph and a target graph. The source graph is representative of a source map and the target graph is representative of a target map and includes nodes and edges that connect the nodes. The method further includes processing each of the source graph and the target graph in a graph convolutional layer to provide graph convolutional layer outputs related to the source graph and to the target graph, processing each of the graph convolutional layer outputs for the source graph and the target graph in a linear rectifying layer to output node feature maps related to the source graph and the target graph. The method further includes selecting pairs of node representations from the node feature maps related to the source graph and the target graph and concatenating the selected pairs to output selected and concatenated pairs of node representations.

Abstract: A map fusing method includes receiving a source graph and a target graph. The source graph is representative of a source map and the target graph is representative of a target map and includes nodes and edges that connect the nodes. The method further includes processing each of the source graph and the target graph in a graph convolutional layer to provide graph convolutional layer outputs related to the source graph and to the target graph, processing each of the graph convolutional layer outputs for the source graph and the target graph in a linear rectifying layer to output node feature maps related to the source graph and the target graph. The method further includes selecting pairs of node representations from the node feature maps related to the source graph and the target graph and concatenating the selected pairs to output selected and concatenated pairs of node representations.

Abstract: Propagating data in a technical network by considering runtime requirements. A component tree data structure is generated for a probabilistic graph representing the technical network and its technical constraints. On the component tree a propagation algorithm is applied, which iteratively determines an optimal edge in the generated component tree, which maximizes an expected information flow to a query node to and/or from each network node by considering the technical network constraints and by executing a Monte-Carlo sampling for estimation of the expected information flow for the cyclic components and by computing the expected information flow of the non-cyclic components analytically and which updates the component tree iteratively with each determined optimal edge and re-estimates the expected information flow in the updated component tree for providing a result with nodes in the technical network for data propagation, so that information flow is maximized by considering technical network constraints.