Inexact Attributed Subgraph Matching

Tu, Thomas K.; Moorman, Jacob D.; Yang, Dongyun; Chen, Qinyi; Bertozzi, Andrea L.

doi:10.1109/bigdata50022.2020.9377872

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…The exact form of the penalty varies across applications and there are a myriad of approaches many taking inspiration from the exact matching problem. These involve a variety of techniques including filtering [40], [47], A* search [39], indexing [32], and continuous techniques [44]. We will not be considering this problem in this paper.…”

Section: A Related Workmentioning

confidence: 99%

“…More recently, subgraph matching arises as a component in motif discovery [23], [37], where frequent subgraphs are uncovered for graph analysis in domains including social networks and biochemical data. Additionally, subgraph matching is relevant in knowledge graph searches, wherein incomplete factual statements are completed by querying a knowledge database [8], [47].…”

Section: Introductionmentioning

confidence: 99%

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Structural Equivalence in Subgraph Matching

Yang¹,

Ge²,

Nguyen³

et al. 2023

Preprint

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Symmetry plays a major role in subgraph matching both in the description of the graphs in question and in how it confounds the search process. This work addresses how to quantify these effects and how to use symmetries to increase the efficiency of subgraph isomorphism algorithms. We introduce rigorous definitions of structural equivalence and establish conditions for when it can be safely used to generate more solutions. We illustrate how to adapt standard search routines to utilize these symmetries to accelerate search and compactly describe the solution space. We then adapt a state-of-the-art solver and perform a comprehensive series of tests to demonstrate these methods' efficacy on a standard benchmark set. We extend these methods to multiplex graphs and present results on large multiplex networks drawn from transportation systems, social media, adversarial attacks, and knowledge graphs.

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Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural Equivalence in Subgraph Matching

Yang¹,

Ge²,

Nguyen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Multiplex graph matching matched filters

et al. 2022

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We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the graph matching matched filter framework of Sussman et al. (IEEE Trans Pattern Anal Mach Intell 42(11):2887–2900, 2019) to the multiplex setting, utilizes a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches. The effectiveness of our approach is demonstrated both theoretically and empirically, with particular attention paid to the potential benefits of considering multiple channels in the multiplex framework.

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