Year: 2024
Type: preprint
Abstract: Given an undirected graph, the $k$-core is a subgraph in which each node has at least $k$ connections. This is widely used in graph analytics to identify core subgraphs within a larger graph. The sequential $k$-core decomposition algorithm faces limitations due to memory constraints, and many data graphs are inherently distributed. A distributed approach is proposed to overcome limitations by allowing each vertex to compute its core number independently using only local information. This work explores the experimental evaluation of a distributed $k$-core decomposition algorithm. By assuming that each vertex is a client as a single computing unit, we simulate the process using Golang, leveraging its Goroutines and message passing. Since real-world data graphs can be large with millions of vertices, it is expensive to build a distributed environment with millions of clients if experiments were run in a real distributed environment. Therefore, our experimental simulation can effectively evaluate the running time and message passing for the distributed $k$-core decomposition.
Source: arXiv (Cornell University)
Authors: Guo, Bin, Zhao, Runze
Institutions: Trent University, McMaster University
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4400065113