Large-Scale Community Detection on YouTube for Topic Discovery and Exploration
By Ullas Gargi, Wenjun Lu, Vahab S. Mirrokni, Sangho Yoon. 2011
102 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Statistical and Nonlinear Physics
- Austin R. Benson
- Jure Leskovec
- David F. Gleich
- Aram Galstyan
- Fred Morstatter
- Ninareh Mehrabi
- Nanyun Peng
- Hao Yin
Citations in the corpus
- Debiasing Community Detection: The Importance of Lowly Connected Nodes (2019, methodology): “ommunity detection methods with low recall scores can suffer from incorporating bias by excluding so many significant users. Although there are many different methods for the community detection task [9, 17], we divided our analysis in two different sections: (1) Methods that do not use node attribu”
- Local Higher-Order Graph Clustering (2017, methodology): “Because of its speed and scalability, this approach is frequently used in applications including ranking and community detection on the Web [13, 16], social net-”