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A number of applications in social networks, telecommunications, and mobile computing create massive streams of graphs. In many such applications, it is useful to detect structural abnormalities which are different from the “typical” behavior of the underlying network. In this paper, we will provide first results on the problem of structural outlier detection in massive network streams. Such problems...
This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value to each data example in the training data, which measures the strength of the corresponding class label. Our proposed method works in two steps. Firstly, we generate a pseudo training...
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