Experimental Evaluation of Distributed k-Core Decomposition
<|reference_start|>Experimental Evaluation of Distributed k-Core Decomposition: Given an undirected graph, the $k$-core is a subgraph in which each node has at least $k$ connections, which 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 data graphs can be inherently distributed. A distributed approach is proposed to overcome limitations by allowing each vertex to independently do calculation by only using local information. This paper 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 Goroutine and message passing. Due to the fact that the real-world data graphs can be large with millions of vertices, it is expensive to build such a distributed environment with millions of clients if the experiments run in a real-life scenario. Therefore, our experimental simulation can effectively evaluate the running time and message passing for the distributed $k$-core decomposition.<|reference_end|>
@article{guo2024experimental,
    title={Experimental Evaluation of Distributed k-Core Decomposition},
    author={Bin Guo, Runze Zhao},
    journal={arXiv preprint arXiv:2406.17580},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17580},
    primaryClass={cs.DC}
}