Large-Scale Community Detection on YouTube for Topic Discovery and Exploration

Abstract

Detecting coherent, well-connected communities in large graphs provides insight into the graph structure and can serve as the basis for content discovery. Clustering is a popular technique for community detection but global algorithms that examine the entire graph do not scale. Local algorithms are highly parallelizable but perform sub-optimally, especially in applications where we need to optimize multiple metrics. We present a multi-stage algorithm based on local-clustering that is highly scalable, combining a pre-processing stage, a lo- cal clustering stage, and a post-processing stage. We apply it to the YouTube video graph to generate named clusters of videos with coherent content. We formalize coverage, co- herence, and connectivity metrics and evaluate the quality of the algorithm for large YouTube graphs. Our use of local algorithms for global clustering, and its implementation and practical evaluation on such a large scale is a first of its kind.