Summary of Graphrpm: Risk Pattern Mining on Industrial Large Attributed Graphs, by Sheng Tian et al.
GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs
by Sheng Tian, Xintan Zeng, Yifei Hu, Baokun Wang, Yongchao Liu, Yue Jin, Changhua Meng, Chuntao Hong, Tianyi Zhang, Weiqiang Wang
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces GraphRPM, a novel parallel and distributed framework for mining graph patterns in large attributed graphs. The authors draw on their expertise in machine learning to tackle the challenges of pattern discovery in massive datasets characteristic of industrial settings. The framework leverages edge-involved graph isomorphism networks and optimized operations for parallel graph computation, leading to significant reductions in computational complexity and resource expenditure. The authors also propose evaluation metrics for intelligent filtration of efficacious risky graph patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps companies analyze large amounts of data about users, which can reveal important insights about behavior and relationships. This is particularly useful for tasks like identifying fraudulent transactions or criminal networks. However, analyzing these massive datasets requires special skills and knowledge. The authors created a new tool called GraphRPM that makes it easier to find patterns in this type of data. |
Keywords
* Artificial intelligence * Machine learning
