Longbing Cao, Philip S Yu, Guansong Pang, Behavior Analytics: Methods and Applications

Complex behaviors are widely seen in artificial and natural intelligent systems, on the internet, social and online networks, multi-agent systems, and brain systems. The in-depth understanding of complex behaviors has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues. However, traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. The so-called behavior analysis in data analytics and learning often focuses on human demographic and business usage data, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle, dynamics and impact on complex problems and business issues. As shown in Fig. 1, we could develop two directions to explicate a global picture of the behavior analytics: qualitative and quantitative behavior analytics. The qualitative analytics addresses the task of behavior reasoning and verification, while the quantitative research targets behavior learning and evaluation. Finally, an appropriate way could be chosen to integrate these two studies to obtain an integrated understanding of both explicit and implicit complex behaviors from both qualitative and quantitative aspects. In this tutorial, we will present an overview of behavior analytics, and discuss complex behavior representation, behavioral feature construction, behavior impact analysis, behavior pattern analysis, negative behavior analysis, behavior interaction and evolution, high-impact behavior analysis, high utility behavior analysis, group and coupled behavior analysis, next best-action recommendation, etc. Several real-world case studies will be demonstrated, including analyzing exceptional financial trading behaviors, detecting abnormal pool manipulation behaviors, mining for high impact social security behavior patterns, analyzing student learning progression, and analyzing online banking behavior interactions. We will show that in-depth behavior analytics creates new opportunities, directions and means for qualitative and quantitative, formal and systematic modeling, learning and analysis of complex behaviors in both physical and virtual organizations.

Also refer to IJCAI2013 tutorial: behavior informatics – modeling, analysis and mining of complex behaviors