The most fascinating aspect of graphs is their ability to encode the information contained in the inner structural organization between its constituting elements. Learning from graphs belong to the so-called Structural Pattern Recognition, from which Graph Embedding emerged as a successful method for processing graphs by evaluating their dissimilarity in a suitable geometric space. In this paper, we investigate the possibility to perform the embedding into a geometric space by leveraging to peculiar constituent graph substructures extracted from training set, namely the maximal cliques, and providing the performances obtained under three main aspects concerning classification capabilities, running times and model complexity. Thanks to a Granular Computing approach, the employed methodology can be seen as a powerful framework able to synthesize models suitable to be interpreted by field-experts, pushing the boundary towards new frontiers in the field of explainable AI and knowledge discovery also in big data contexts.

Exploiting cliques for granular computing-based graph classification / Baldini, Luca; Martino, Alessio; Rizzi, Antonello. - 2020 International Joint Conference on Neural Networks (IJCNN), (2020), pp. 1-9. (IJCNN 2020 - 2020 International Joint Conference on Neural Networks, Online Event due to COVID-19 (formerly Glasgow, UK), 19-24 July 2020). [10.1109/IJCNN48605.2020.9206690].

Exploiting cliques for granular computing-based graph classification

Alessio Martino;
2020

Abstract

The most fascinating aspect of graphs is their ability to encode the information contained in the inner structural organization between its constituting elements. Learning from graphs belong to the so-called Structural Pattern Recognition, from which Graph Embedding emerged as a successful method for processing graphs by evaluating their dissimilarity in a suitable geometric space. In this paper, we investigate the possibility to perform the embedding into a geometric space by leveraging to peculiar constituent graph substructures extracted from training set, namely the maximal cliques, and providing the performances obtained under three main aspects concerning classification capabilities, running times and model complexity. Thanks to a Granular Computing approach, the employed methodology can be seen as a powerful framework able to synthesize models suitable to be interpreted by field-experts, pushing the boundary towards new frontiers in the field of explainable AI and knowledge discovery also in big data contexts.
2020
978-1-7281-6926-2
embedding spaces
granular computing
graph edit distances
structural pattern recognition
supervised learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/214576
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