Incremental Community Detection in Distributed Dynamic Graph
This work addresses the challenge of efficiently updating communities in dynamic graphs for applications in graph analytics, but it is incremental as it builds on existing distributed methods.
The paper tackles the problem of community detection in dynamic graphs that change over time with streaming data, proposing an incremental algorithm (IDWCC) that performs up to three times faster than a baseline distributed algorithm (DWCC) while maintaining similar accuracy.
Community detection is an important research topic in graph analytics that has a wide range of applications. A variety of static community detection algorithms and quality metrics were developed in the past few years. However, most real-world graphs are not static and often change over time. In the case of streaming data, communities in the associated graph need to be updated either continuously or whenever new data streams are added to the graph, which poses a much greater challenge in devising good community detection algorithms for maintaining dynamic graphs over streaming data. In this paper, we propose an incremental community detection algorithm for maintaining a dynamic graph over streaming data. The contributions of this study include (a) the implementation of a Distributed Weighted Community Clustering (DWCC) algorithm, (b) the design and implementation of a novel Incremental Distributed Weighted Community Clustering (IDWCC) algorithm, and (c) an experimental study to compare the performance of our IDWCC algorithm with the DWCC algorithm. We validate the functionality and efficiency of our framework in processing streaming data and performing large in-memory distributed dynamic graph analytics. The results demonstrate that our IDWCC algorithm performs up to three times faster than the DWCC algorithm for a similar accuracy.