Abstract
Graph pattern matching is a powerful mechanism for searching on network data. Most of the graph pattern matching tools available are based on subgraph isomorphism, i.e. finding a one-to-one correspondence between nodes of a query graph and nodes of a target graph. Often this approach is not flexible enough, since it does not allow the query to represent sets of nodes of any size that share the same profile of connectivity. We propose a novel powerful graph matching approach that overcomes the existing limits and allows the user to define complex patterns in a simple and intuitive way. In our approach, queries are expressed as graphs, whose nodes and edges specify structural constraints and filtering criteria. We show that, despite its simplicity, the proposed approach can solve a large variety of practical problems.
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Our approach can also be applied to undirected graphs or multi-graphs. For simplicity of exposition, in this paper we refer to directed graphs only.
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In this formulation we do not make any distinction between fixed nodes and variable nodes since the number of candidate valid associations depends on f.
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Filtering conditions can be imposed on edges as well, though for simplicity of exposition this is not considered in our description.
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Acknowledgement
This work is supported by the CLARA - CLoud plAtform and smart underground imaging for natural Risk Assessment - project, SCN 00451, funded by the Italian Ministry of Education, Universities and Research, within the “Smart Cities and Communities and Social Innovation” initiative.
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Fornaia, A., Mongiovì, M., Pappalardo, G., Tramontana, E. (2019). A General Powerful Graph Pattern Matching System for Data Analysis. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_4
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DOI: https://doi.org/10.1007/978-3-030-05414-4_4
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