Weighted Micro-Clustering: Application to Community Detection in Large-Scale Co-Purchasing Networks with User Attributes
WWW 2016 (The 25th International Conference on World Wide Web) Posters, 2016/4
機械学習 (Machine Learning) データサイエンス (Data Science)
- We propose a simple and scalable method for soft community detection that makes use of both graph structures and vertex attributes. Our method is based on micro-clustering, which is a scalable and efficient clique-based method for detecting overlapping communities in unweighted graphs. We extend this method to graphs with vertex attributes so that we can make use of information supplied by vertex attributes. Our method still requires the same time complexity as micro-clustering. We confirm the validity and efficiency of our method by applying it to a large-scale co-purchasing network of real online auction data.
Weighted Micro-Clustering: Application to Community Detection in Large-Scale Co-Purchasing Networks with User Attributes（PDF 435）