Title
Weighted Micro-Clustering: Application to Community Detection in Large-Scale Co-Purchasing Networks with User Attributes.
Abstract
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.
Year
DOI
Venue
2016
10.1145/2872518.2889406
WWW (Companion Volume)
Field
DocType
Citations 
Graph,Data mining,Clique,Vertex (geometry),Computer science,Purchasing,Cluster analysis,Time complexity,Online auction,Scalability
Conference
1
PageRank 
References 
Authors
0.48
3
4
Name
Order
Citations
PageRank
Tomoya Yamazaki110.48
Nobuyuki Shimizu2377.76
hayato kobayashi3296.07
Satoshi Yamauchi410.48