Parallel Batch-Dynamic Algorithms for $k$-Core Decomposition and Related Graph Problems

Liu, Quanquan C.; Shi, Jessica; Yu, Shangdi; Dhulipala, Laxman; Shun, Julian

doi:10.48550/arxiv.2106.03824

Cited by 1 publication

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“…In [26], Sun et al design algorithms to maintain approximate core numbers in dynamic hypergraphs. In [27], Liu et al propose a parallel and batchdynamic algorithm for approximate k-core decomposition and maintenance.…”

Section: Core Maintenancementioning

confidence: 99%

Simplified Algorithms for Order-Based CoreMaintenance

Guo

Sekerinski

2023

Preprint

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Graph analytics attract much attention from both research and industry communities. Due to its linear time complexity, the $k$-core decomposition is widely used in many real-world applications such as biology, social networks, community detection, ecology, and information spreading. In many such applications, the data graphs continuously change over time. The changes correspond to edge insertion and removal. Instead of recomputing the $k$-core, which is time-consuming, we study how to maintain the $k$-core efficiently. That is, when inserting or deleting an edge, we need to identify the affected vertices by searching for more vertices. The state-of-the-art order-based method maintains an order, the so-called $k$-order, among all vertices, which can significantly reduce the searching space. However, this order-based method is complicated to understand and implement, and its correctness is not formally discussed.In this work, we propose a simplified order-based approach by introducing the classical Order Data Structure to maintain the $k$-order, which significantly improves the worst-case time complexity for both edge insertion and removal algorithms. Also, our simplified method is intuitive to understand and implement; it is easy to argue the correctness formally. Additionally, we discuss a simplified batch insertion approach.The experiments evaluate our simplified method over 12 real and synthetic graphs with billions of vertices. Compared with the existing method, our simplified approach achieves high speedups up to 7.7x and 9.7x for edge insertion and removal, respectively.

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Section: Core Maintenancementioning

confidence: 99%