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A Granular Computing approach to the design of optimized graph classification systems

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Abstract

Research on Graph-based pattern recognition and Soft Computing systems has attracted many scientists and engineers in several different contexts. This fact is motivated by the reason that graphs are general structures able to encode both topological and semantic information in data. While the data modeling properties of graphs are of indisputable power, there are still different concerns about the best way to compute similarity functions in an effective and efficient manner. To this end, suited transformation procedures are usually conceived to address the well-known Inexact Graph Matching problem in an explicit embedding space. In this paper, we propose two graph embedding algorithms based on the Granular Computing paradigm, which are engineered as key procedures of a general-purpose graph classification system. Tests have been conducted on benchmarking datasets relying on both synthetic and real-world data, achieving competitive results in terms of test set classification accuracy.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics, and Telecommunications, SAPIENZA University of Rome, Via Eudossiana 18, 00184, Rome, Italy

    Filippo Maria Bianchi, Lorenzo Livi & Antonello Rizzi

  2. Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada

    Alireza Sadeghian

Authors
  1. Filippo Maria Bianchi
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  2. Lorenzo Livi
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  3. Antonello Rizzi
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  4. Alireza Sadeghian
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Correspondence to Lorenzo Livi.

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Communicated by W. Pedrycz.

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Bianchi, F.M., Livi, L., Rizzi, A. et al. A Granular Computing approach to the design of optimized graph classification systems. Soft Comput 18, 393–412 (2014). https://doi.org/10.1007/s00500-013-1065-z

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