An Algorithm for Mining Top K Influential Community Based Evolutionary Outliers in Temporal Dataset

Abstract

Identifying outlier objects against main community evolution trends is not only meaningful itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. With the definition of community belongingness matrix of data objects, we constructed the transition matrix to least square optimize the pattern of evolutionary quantity between two consecutive belongingness snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the step by step community membership change. The transition matrix is further optimized using robust regression methods by minimizing the disturbance incurred by the outliers, and the outlier factor of the anomalous object was defined. Being aware that large proportion of trivial but nomadic objects may exist in large datasets. This paper focus only on the influential community evolutionary outliers which both show remarkable difference from the main body of their community and sharp changes of their membership role within the communities. An algorithm on detection such kind of outliers are purposed in the paper. Experimental results on both synthetic and real world datasets show that the proposed approach is highly effective and efficient in discovering reasonable influential evolutionary community outliers.