Simplified Algorithms for Order-Based Core Maintenance

TL;DR

This paper introduces a simplified, efficient order-based algorithm for maintaining the $k$-core in dynamic graphs, improving speed and ease of implementation over previous methods, with formal correctness and batch update support.

Contribution

The paper proposes a simplified order-based $k$-core maintenance algorithm using classical data structures, making it more understandable, easier to implement, and more efficient than existing approaches.

Findings

Achieves up to 7.7x speedup in edge insertion

Achieves up to 9.7x speedup in edge removal

Validated on 12 real and synthetic graphs with billions of vertices

Abstract

Graph analytics attract much attention from both research and industry communities. Due to the 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 for understanding and implementation, 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.