
MStream: Fast Streaming MultiAspect Group Anomaly Detection
Given a stream of entries in a multiaspect data setting i.e., entries h...
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Isconna: Streaming Anomaly Detection with Frequency and Patterns
An edge stream is a common form of presentation of dynamic networks. It ...
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MIDAS: MicroclusterBased Detector of Anomalies in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign an...
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RealTime Streaming Anomaly Detection in Dynamic Graphs
Given a stream of graph edges from a dynamic graph, how can we assign an...
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MultiLevel Anomaly Detection on TimeVarying Graph Data
This work presents a novel modeling and analysis framework for graph seq...
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Mining Dense Subgraphs with Similar Edges
When searching for interesting structures in graphs, it is often importa...
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Computing Graph Descriptors on Edge Streams
Graph feature extraction is a fundamental task in graphs analytics. Usin...
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SketchBased Streaming Anomaly Detection in Dynamic Graphs
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing work seeks to detect either anomalous edges or anomalous subgraphs, but not both. In this paper, we first extend the countmin sketch data structure to a higherorder sketch. This higherorder sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure). We then propose four online algorithms that utilize this enhanced data structure, which (a) detect both edge and graph anomalies; (b) process each edge and graph in constant memory and constant update time per newly arriving edge, and; (c) outperform stateoftheart baselines on four realworld datasets. Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.
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