An Efficient and Spam-Robust Proximity Measure Between Communication Entities

Electronic communication service providers are obliged to retain communication data for a certain amount of time by their local laws. The retained communication data or the communication logs are used in various applications such as crime detection, viral marketing, analytical study, and so on. Many...

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Bibliographic Details
Published in:Journal of computer science and technology 2013-03, Vol.28 (2), p.394-400
Main Authors: Jeon, Joo Hyuk, Song, Jihwan, Kwon, Jeong Eun, Lee, Yoon Joon, Park, Man Ho, Kim, Myoung Ho
Format: Article
Language:English
Subjects:
Analysis
Artificial Intelligence
Communication
Communications industry
Computational efficiency
Computer Science
Conduction
Data Structures and Information Theory
Electronics
Information Systems Applications (incl.Internet)
Logs
Marketing
Mathematical analysis
Proximity
Regulation
Robustness (mathematics)
Short Paper
Shortest-path problems
Social network analysis
Social networks
Software Engineering
Studies
Synthetic data
Theory of Computation
Viral marketing
垃圾邮件
应用程序
技术分析
最短路径
测量
计算效率
通信实体
通信服务
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Summary:Electronic communication service providers are obliged to retain communication data for a certain amount of time by their local laws. The retained communication data or the communication logs are used in various applications such as crime detection, viral marketing, analytical study, and so on. Many of these applications rely on effective techniques for analyzing communication logs. In this paper, we focus on measuring the proximity between two communication entities, which is a fundamental and important step toward further analysis of communication logs, and propose a new proximity measure called ESP （Efficient and Spam-Robust Proximity measure）. Our proposed measure considers only the （graph- theoretically） shortest paths between two entities and gives small values to those between spam-like entities and others. Thus, it is not only computationally efficient but also spam-robust. By conducting several experiments on real and synthetic datasets, we show that our proposed proximity measure is more accurate, computationally efficient and spam-robust than the existing measures in most cases.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-013-1339-z