论文标题
利用关系序列的多层次依赖性进行社会垃圾邮件发送者检测
Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection
论文作者
论文摘要
最近的许多研究揭示了与关系依赖性但与内容无关的社交垃圾邮件发送者检测框架的发展。这主要是因为当垃圾邮件发送者试图掩盖其恶意意图时，很难改变用户之间的关系。我们的研究研究了在多关系社交网络的背景下调查垃圾邮件发送者检测问题，并试图完全利用异质关系的序列以提高检测准确性。具体而言，我们介绍了多级依赖模型（MDM）。 MDM能够利用用户在其关系序列中隐藏的长期依赖性以及短期依赖性。此外，由于短期序列的类型是多重折叠的事实，MDM从个人级别和工会级别的角度完全考虑了短期关系序列。现实世界中的多个关系社交网络的实验结果证明了我们提出的MDM对多关系社会垃圾邮件发送者检测的有效性。
Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intents. Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection.
