SCoDa: Scalable Community Detection in Data Streams

Community detection is a fundamental task in the analysis of large-scale dynamic networks. As graphs continuously evolve through edge insertions and deletions, maintaining high-quality community structures with low latency remains a significant computational challenge. We introduce SCoDa, a fully dynamic parallel algorithm for community detection that supports real-time streaming updates in both edge addition and deletion scenarios. SCoDa combines a GPU-optimized Louvain algorithm for initial static partitioning with efficient, parallel update routines that apply updated affected regions computations to selectively recompute only the affected regions of the graph. This localized update strategy minimizes redundant processing and maximizes scalability.By leveraging fine-grained parallelism on modern GPUs, SCoDa enables low-latency updates and high-throughput processing of streaming graph modifications. We evaluate our approach on large-scale real-world datasets from SNAP and SuiteSparse, with graphs scaling up to 42.6 billion edges, spanning domains like social media, communication, road networks, and the web. Experimental results show that SCoDa achieves an average 1.7× speedup over existing parallel baselines during update computation, while maintaining modularity and community quality. These findings underscore SCoDa’s scalability and effectiveness for real-time dynamic graph analytics at a massive scale.