HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses

The analysis of sequential patterns is a prominent research topic. In this paper, we provide a formalization of a graph-based approach, such that a directed weighted graph/network can be extended using a sequential state transformation function, that “interprets” the network in order to model state transition matrices. We exemplify the approach for deriving such interpretations, in order to assess these and according hypotheses in an industrial application context. Specifically, we present and discuss results of applying the proposed approach for topology and anomaly analytics in a large-scale real-world sensor-network.

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