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| Content Provider | ACM Digital Library |
|---|---|
| Author | Wakabayashi, Kei Nozawa, Kento |
| Abstract | In the data mining field, community detection, which decomposes a graph into multiple subgraphs, is one of the major techniques to analyze graph data. In recent years, the scalability of the community detection algorithm has been a crucial issue because of the growing size of real-world networks such as the co-author network and web graph. In this paper, we propose a scalable overlapping community detection method by using the stochastic variational Bayesian training of latent Dirichlet allocation (LDA) models, which predicts sets of neighbor nodes with a community mixture distribution. In the experiment, we show that the proposed method is much faster than previous methods and is capable of detecting communities even in a huge network that contains 60 million nodes and 1.8 billion edges. Furthermore, we compared different mini-batch sizes and the number of iterations in stochastic variational Bayesian inference to determine an empirical trade-off between efficiency and quality of overlapping community detection. |
| Starting Page | 9 |
| Ending Page | 16 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450352406 |
| DOI | 10.1145/3057148.3057150 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-02-10 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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