  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.
