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Discovering subjectively interesting multigraph patterns
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DOI：10.1007/s10994-020-05873-9.png)
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Abstract
En 中文
Over the past decade, network analysis has attracted substantial interest because of its potential to solve many real-world problems. This paper lays the conceptual foundation for an application in aviation, through focusing on the discovery of patterns in multigraphs (graphs in which multiple edges can be present between vertices). Our main contributions are twofold. Firstly, we propose a novel subjective interestingness measure for patterns in both undirected and directed multigraphs. Though this proposal is inspired by our previous related research for simple graphs (having only single edges), the properties of multigraphs make this transition challenging. Secondly, we propose a greedy algorithm for subjectively interesting pattern mining, and demonstrate its efficacy through several experiments on synthetic and real-world examples. We conclude with a case study in aviation, which demonstrates how the departure from an analyst's prior beliefs captured as subjectively interesting patterns could help improve an analyst's understanding of the data and problem at hand.
Keywords:
Multigraph
Subjective interestingness
Maximum entropy principle
Exploratory data mining
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