@inproceedings{d4651146185242cd8f5221b49439e880,
title = "DETER: Streaming Graph Partitioning via Combined Degree and Cluster Information",
abstract = "Efficient graph partitioning plays an important role in distributed graph processing systems with the rapid growth of the scale of graph data. The quality of partitioning affects the performance of systems greatly. However, most existing vertex-cut graph partitioning algorithms only focused on degree information and ignored the cluster information of a coming edge when assigning edges. It is beneficial to assign an edge to a partition with more neighbors because keeping a dense subgraph in one partition would reduce the communication cost. In this paper, we propose DETER, an efficient vertex-cut streaming graph partitioning algorithm that takes both degree and cluster information into account when assigning an edge to one partition. Our evaluations suggest that DETER algorithm owns the ability to efficiently partition large graphs and reduce communication cost significantly compared to state-of-the-art graph partitioning algorithms.",
keywords = "Distributed graph computing, Graph partitioning, Streaming, Vertex-cut",
author = "Cong Hu and Jiang Zhong and Qi Li and Qing Li",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 19th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2019 ; Conference date: 09-12-2019 Through 11-12-2019",
year = "2020",
doi = "10.1007/978-3-030-38991-8\_16",
language = "英语",
isbn = "9783030389901",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "242--255",
editor = "Sheng Wen and Albert Zomaya and Yang, \{Laurence T.\}",
booktitle = "Algorithms and Architectures for Parallel Processing - 19th International Conference, ICA3PP 2019, Proceedings",
}