A High‐Scalability Graph Modification System for Large‐Scale Networks

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dc.contributor.authorXu, Shaobinen_US
dc.contributor.authorSun, Minghuien_US
dc.contributor.authorQin, Junen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWimmer, Michaelen_US
dc.date.accessioned2024-12-19T11:15:16Z
dc.date.available2024-12-19T11:15:16Z
dc.date.issued2024
dc.description.abstractModifying network results is the most intuitive way to inject domain knowledge into network detection algorithms to improve their performance. While advances in computation scalability have made detecting large‐scale networks possible, the human ability to modify such networks has not scaled accordingly, resulting in a huge ‘interaction gap’. Most existing works only support navigating and modifying edges one by one in a graph visualization, which causes a significant interaction burden when faced with large‐scale networks. In this work, we propose a novel graph pattern mining algorithm based on the minimum description length (MDL) principle to partition and summarize multi‐feature and isomorphic sub‐graph matches. The mined sub‐graph patterns can be utilized as mediums for modifying large‐scale networks. Combining two traditional approaches, we introduce a new coarse‐middle‐fine graph modification paradigm (. query graph‐based modification sub‐graph pattern‐based modification raw edge‐based modification). We further present a graph modification system that supports the graph modification paradigm for improving the scalability of modifying detected large‐scale networks. We evaluate the performance of our graph pattern mining algorithm through an experimental study, demonstrate the usefulness of our system through a case study, and illustrate the efficiency of our graph modification paradigm through a user study.en_US
dc.description.number6
dc.description.sectionheadersORIGINAL ARTICLES
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15191
dc.identifier.pages16 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15191
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15191
dc.publisher© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectgraph querying
dc.subjectgraph pattern mining
dc.subjectgraph modification paradigm
dc.subjectgraph modification system
dc.titleA High‐Scalability Graph Modification System for Large‐Scale Networksen_US
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