A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks
A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks
复制标题
社交网络中商业应用的机器学习和数据挖掘方法综述
DOI：
10.4018/jebr.2013010103
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Biba
中科院分区：
文献类型：
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作者：
Evis Trandafili;M. Biba
社交网络具有突出的营销价值，开发用于病毒式营销的数据挖掘方法是研究界的热点。然而，大多数社交网络仍然不可能被完全分析和理解，这是由于传统的机器学习和数据挖掘方法的禁止大小和能力，以处理与产生数据的大规模环境相关的学习过程中的新维度。一方面，这种网络的诞生和发展给学习和挖掘社区带来了巨大的挑战，另一方面也为非常强大的商业应用程序提供了可能。然而，对这些商业应用程序以及社交网络挖掘促进营销的潜力了解甚少。本文综述了机器学习和数据挖掘领域中有关社交网络分析及其商业应用的最重要的最新方法。作者回顾了与社交网络相关的问题，并描述了该领域的最新发展，讨论了社交网络分析的重要成就，并概述了未来的工作。审查的重点不仅是应用于社交网络的学习和挖掘方法的技术方面，而且还包括这些方法的商业潜力。
Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.
在大规模社交网络中寻找社区结构
DOI：
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发表时间：
2007
期刊：
Proceedings of the Sixteenth International World Wide Web Conference
影响因子：
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作者：
Ken Wakita;Toshiuiki Tsurumi
通讯作者：
Toshiuiki Tsurumi