Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44244
Title: Shaping graph pattern mining for financial risk
Authors: Ribeiro, Bernardete 
Chen, Ning 
Kovacec, Alexander 
Keywords: Graph mining; Classification; Financial risk
Issue Date: 2017
Publisher: Elsevier B.V.
Serial title, monograph or event: Neurocomputing
Abstract: In recent years graph pattern mining took a prominent role in knowledge discovery in many scientific fields. From Web advertising to biology and finance, graph data is ubiquitous making pattern-based graph tools increasingly important. When it comes to financial settings, data is very complex and although many successful approaches have been proposed often they neglect the intertwined economic risk factors, which seriously affects the goodness of predictions. In this paper, we posit that financial risk analysis can be leveraged if structure can be taken into account by discovering financial motifs. We look at this problem from a graph-based perspective in two ways, by considering the structure in the inputs, the graphs themselves, and by taking into account the graph embedded structure of the data. In the first, we use gBoost combined with a substructure mining algorithm. In the second, we take a subspace learning graph embedded approach. In our experiments two datasets are used: a qualitative bankruptcy data benchmark and a real-world French database of corporate companies. Furthermore, we propose a graph construction algorithm to extract graph structure from feature vector data. Finally, we empirically show that in both graph-based approaches the financial motifs are crucial for the classification, thereby enhancing the prediction results.
URI: http://hdl.handle.net/10316/44244
DOI: 10.1016/j.neucom.2017.01.119
Rights: embargoedAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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