Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features
Open AccessThe ability to mine data represented as a graph has become important in several domains for detecting various structural patterns, but less work has been done in terms of detecting anomalies ingraph-based data. Also, time-series of graphs are becoming more and more common, for example, communication graphs, social networks, etc., and methods for statistical inferences are demanding. While there has been some previous work that used statisticalmetrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains. Moreover, most anomaly-detection methods use a supervised approach, which requires some sort of baseline of information from which comparisons or training can be performed. In general, if one has an idea what is normal behavior, deviations from that behavior could constitute an anomaly. However, the issue with those approaches is that one has to train the system, and the data has to already be labeled. It is also known that no single graph feature is uniformly most powerful.This research introduced a theory of scan statistics on time seriesof graphs and hypergraphs to investigate an effectiveness onanomaly/change point detection problem. The primary researchhypothesis of this work is that scan statistic is capable ofdetecting certain types of anomalies that are not apparent by usingother techniques. Also, by combining multiple graph features, theperformance of statistical inference can be improved compared to amethod that only uses an individual feature separately.The result shows that the proposed statistics on time series of graphsand hypergraphs outperforms on certain anomalies. It is furtherdemonstrated that a fusion statistic can provide superior inferencecompared to individual features alone. The major contributions of this work are the introduction of a newgraph feature on detecting anomaly in time series of graphs andhypergraphs, and the confirmation of adaptive weighting as amechanism for combination of features.
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