Name
Abstract | ||
|---|---|---|
A large payment network contains millions of merchants and billions of transactions, and the merchants are described in a large number of attributes with incomplete values. Understanding its community structures is crucial to ensure its sustainable and long lasting. Knowing a merchant's community is also important from many applications - risk management, compliance, legal and marketing. To detect communities, an algorithm has to take advances from both attribute and topological information. Further, the method has to be able to handle incomplete and complex attributes. In this paper, we propose a framework named AGGMMR to effectively address the challenges come from scalability, mixed attributes, and incomplete value. We evaluate our proposed framework on four benchmark datasets against five strong baselines. More importantly, we provide a case study of running AGGMMR on a large network from PayPal which contains $100 million$ merchants with $1.5 billion$ transactions. The results demonstrate AGGMMR's effectiveness and practicability.
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Year | DOI | Keywords |
|---|---|---|
2019 | 10.1145/3292500.3330721 | community detection, complex attributes, large attributed network |
Field | DocType | ISSN |
Data science,Data mining,Topological information,Computer science,Baseline (configuration management),Risk management,Payment,Scalability | Conference | 978-1-4503-6201-6 |
ISBN | Citations | PageRank |
978-1-4503-6201-6 | 4 | 0.38 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Zhe | 1 | 4 | 0.38 |
| Aixin Sun | 2 | 3071 | 156.89 |
| Xiaokui Xiao | 3 | 3266 | 142.32 |