FulBM:基于地标的 3 跳覆盖标记的快速全批量维护

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-03-15 DOI:10.1145/3650035
Wentai Zhang, HaiHong E, HaoRan Luo, Mingzhi Sun
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引用次数: 0

摘要

基于地标的三跳覆盖标注法是在大规模复杂网络上进行最短距离/路径查询的一类方法。它通过离线预计算索引来加速在线距离/路径查询。现实世界中的大多数图的拓扑结构都会发生快速变化,因此有必要对动态图进行索引维护。迄今为止,大多数索引维护方法每次只能处理一条边的更新(添加或删除)。为了适应频繁变化的图,我们研究了 3 跳覆盖标记的完全批量维护问题,并提出了名为 FulBM 的方法。FulBM 由两种算法组成:InsBM和DelBM,分别用于处理边的批量插入和删除。这种分离的原因是,批量维护边缘插入更省时,而且现实世界中的大多数边缘更新都是增量的。InsBM 和 DelBM 都配备了精心设计的剪枝策略,以最大限度地减少顶点访问次数。我们在合成图和真实图上进行了全面的实验,以验证 FulBM 及其变体在加权图上的效率。结果表明,与最先进的方法相比,我们的方法提高了 5.5 到 228 倍的速度。
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FulBM: Fast fully batch maintenance for landmark-based 3-hop cover labeling

Landmark-based 3-hop cover labeling is a category of approaches for shortest distance/path queries on large-scale complex networks. It pre-computes an index offline to accelerate the online distance/path query. Most real-world graphs undergo rapid changes in topology, which makes index maintenance on dynamic graphs necessary. So far, the majority of index maintenance methods can handle only one edge update (either an addition or deletion) each time. To keep up with frequently changing graphs, we research the fully batch maintenance problem for the 3-hop cover labeling, and proposed the method called FulBM. FulBM is composed of two algorithms: InsBM and DelBM, which are designed to handle batch edge insertions and deletions respectively. This separation is motivated by the insight that batch maintenance for edge insertions are much more time-efficient, and the fact that most edge updates in the real world are incremental. Both InsBM and DelBM are equipped with well-designed pruning strategies to minimize the number of vertex accesses. We have conducted comprehensive experiments on both synthetic and real-world graphs to verify the efficiency of FulBM and its variants for weighted graphs. The results show that our methods achieve 5.5 × to 228 × speedup compared with the state-of-the-art method.

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来源期刊
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.70
自引率
5.60%
发文量
172
审稿时长
3 months
期刊介绍: TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.
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