Streaming Graph Clustering for Graph Partition
Zhuoxu Zhang, Zezhong Ding · 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) · 2022
Graph partition, as a mandatory step in the process of large scale graph and distributed graph analysis system, has been heavily examined in the past decade. Generally, they have been categorized into two types, offline and online. As the massive growth of the modern graph, tradition offline methods fail in achieving both effectiveness and efficiency. Hence, there is an urgent need for fast and scalable online graph partition approaches. Most state-of-art online algorithms focus on the design of streaming methods to reach the merit. In such a context, a series of novel approaches emerge recently, most of which concentrate on partitioning the original graph based on the streaming clustering results. These innovative manners have been experimentally proved to have a better performance compared with the mainstream methods. Hence, it is essential to design a high-quality streaming graph clustering algorithm in order to refine these cluster-based partitioners. In this article, we purpose a fine-grained streaming clustering algorithms specifically designed for cluster-based partitioner. Our experimental results demonstrated that the purposed clustering approach can achieve a better replication factor w.r.t the constrained balanced factor compared to the baseline methods.