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Time Series Classification Based on Complex Network, through deep learning, we also introduce an automatic visibility graph (AVG) based on graph neural network (GNN), a framework which can transform time series into graphs and realize classification end-to-end. Finally, we carry out experiments on some common datasets to prove the effectiveness of our methods.

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Graph Data Mining978-981-16-2609-8Series ISSN 2522-0179 Series E-ISSN 2522-0187

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,Einfluß und Auswirkung von Meßfehlern,perform link prediction with pre-defined assumptions and only use superficial structural features. While it is widely acknowledged that a node could be characterized by a bunch of neighbor nodes, network embedding algorithms and newly emerged graph neural networks still exploit structural features o

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Zweiphasenstrom-Leistungsmessungenrks, etc., where subgraphs or motifs can be considered as network building blocks with particular functions to capture mesoscopic structures. Most existing studies ignored the interaction between these subgraphs, which could be of particular importance to represent the global structure at the subgra

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Reproduzierbarkeit der Kammerstrommessungen,of Ethereum transaction records remains under-explored. In this chapter, we model Ethereum transaction records as a complex network and further study the problem of phishing detection and transaction tracking via node classification and link prediction, respectively, which provides a deeper understa