Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Space-efficient sampling from social activity streams
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Detecting abnormal patterns in call graphs based on the aggregation of relevant vertex measures
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Community trend outlier detection using soft temporal pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Mining neighbor-based patterns in data streams
Information Systems
A Repository for Multirelational Dynamic Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Outskewer: Using Skewness to Spot Outliers in Samples and Time Series
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Inferring social roles and statuses in social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient community detection in large networks using content and links
Proceedings of the 22nd international conference on World Wide Web
Anatomy of a web-scale resale market: a data mining approach
Proceedings of the 22nd international conference on World Wide Web
On detecting association-based clique outliers in heterogeneous information networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Mining most frequently changing component in evolving graphs
World Wide Web
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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 are inherently challenging, because the problem of outlier detection is specially challenging because of the high volume of the underlying network stream. The stream scenario also increases the computational challenges for the approach. We use a structural connectivity model in order to define outliers in graph streams. In order to handle the sparsity problem of massive networks, we dynamically partition the network in order to construct statistically robust models of the connectivity behavior. We design a reservoir sampling method in order to maintain structural summaries of the underlying network. These structural summaries are designed in order to create robust, dynamic and efficient models for outlier detection in graph streams. We present experimental results illustrating the effectiveness and efficiency of our approach.