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An Effective Graph Edit Distance Model Using Node Mapping Information
http://doi.org/10.5626/JOK.2025.52.1.88
Graph Edit Distance (GED) is the most representative method for quantifying similarity between graphs. However, calculating an exact GED is an NP-Hard problem, which incurs a prohibitively large amount of computational cost. To efficiently compute GED, recent studies have focused on deriving an approximate GED between graphs using deep learning models. However, existing models tend to exhibit large approximation errors and suffer from insufficient interpretability because they do not consider node-to-node relationships between graphs. To remedy these problems faced by existing models, a model that could learn a mapping matrix through node-level embeddings of two graphs was proposed in this study to provide better interpretability of the GED approximation while minimizing information loss during the learning process. Results of experiments showed that the proposed model consistently outperformed existing models.
Single Group Collective Trip Planning Query Processing Using G-tree Index Structures on Road Networks
http://doi.org/10.5626/JOK.2020.47.5.513
In this paper, we discuss Single Group Collective Trip Planning (SGCTP) queries that minimize the overall travel cost in location-based ride sharing services. The SGCTP queries identify a meeting point that minimizes the overall cost of such a trip when a group of users are gathered at a particular point and travel to the destination using one vehicle. Although many researches on collective trip planning queries have been conducted, there is a problem that the query performance is effective only in a specific situation. So, we introduce a baseline method of the SGCTP queries and then, propose an effective pruning technique with a G-tree index structure. Additionally, we analyze that the limitations of the previous studies, and experimental results show that the proposed pruning technique can obtain the optimal query result without being affected by the limitations of the previous studies.
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