Anomaly detection in dynamic networks: a survey

Ranshous, Stephen; Shen, Shitian; Koutra, Danai; Harenberg, Steve; Faloutsos, Christos; Samatova, Nagiza F.

doi:10.1002/wics.1347

Cited by 293 publications

(197 citation statements)

References 127 publications

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“…Two surveys [35,36] describe various outlier detection methods for static and time-evolving graphs. Here we describe just a small set of approaches.…”

Section: Anomaly Detectionmentioning

confidence: 99%

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

et al. 2017

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Abstract:Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is 'important'? How can multiple researchers explore a new graph dataset collectively and "help" each other with their findings? In this article, we present PERSEUS-HUB, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. PERSEUS-HUB uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In PERSEUS-HUB, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution) marked by other users or experts. Moreover, PERSEUS-HUB guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks.

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“…Two surveys [35,36] describe various outlier detection methods for static and time-evolving graphs. Here we describe just a small set of approaches.…”

Section: Anomaly Detectionmentioning

confidence: 99%

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

et al. 2017

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“…Outlier detection over dynamic graphs has received an increasing amount of attention recently given its practical applications [18]. Traditionally, analysis is performed by examining sequences of graph snapshots, where each snapshot captures the interactions for a given time window (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…As a result of how ubiquitous dynamic graphs have become, there has been a surge of research in developing techniques to attain novel insights into the represented system. In particular, outlier (or anomaly) detection has become a topic of great interest given its many applications [18]. For example:…”

Section: Introductionmentioning

confidence: 99%

A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations

Ranshous¹,

Harenberg²,

Sharma³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detection in dynamic graphs can provide unique insights into the relationships of objects and identify novel or emerging relationships. To date, outlier detection in dynamic graphs has been studied in the context of graph streams, focusing on the analysis and comparison of entire graph objects. However, the volume and velocity of data are necessitating a transition from outlier detection in the context of graph streams to outlier detection in the context of edge streams-where the stream consists of individual graph edges instead of entire graph objects.In this paper, we propose the first approach for outlier detection in edge streams. We first describe a high-level model for outlier detection based on global and local structural properties of a stream. We then propose a novel application of the Count-Min sketch for approximating these properties, and prove probabilistic error bounds on our edge outlier scoring functions. Our sketch-based implementation provides a scalable solution, having constant time updates and constant space requirements. Experiments on synthetic and real-world datasets demonstrate our method's scalability, effectiveness for discovering outliers, and the effects of approximation.

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“…Therefore, a number of researchers are adding dynamic concept into their research work. For example, a number of anomaly detection techniques specially related to dynamic networks are recently surveyed by Ranshous et al [104]. For instance, a scoring function is used to identify various types of anomalies.…”

Section: Other Graph Based Approachesmentioning

confidence: 99%

A survey of data mining and social network analysis based anomaly detection techniques

Kaur

Singh

2016

Egyptian Informatics Journal

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With the increasing trend of online social networks in different domains, social network analysis has recently become the center of research. Online Social Networks (OSNs) have fetched the interest of researchers for their analysis of usage as well as detection of abnormal activities. Anomalous activities in social networks represent unusual and illegal activities exhibiting different behaviors than others present in the same structure. This paper discusses different types of anomalies and their novel categorization based on various characteristics. A review of number of techniques for preventing and detecting anomalies along with underlying assumptions and reasons for the presence of such anomalies is covered in this paper. The paper presents a review of number of data mining approaches used to detect anomalies. A special reference is made to the analysis of social network centric anomaly detection techniques which are broadly classified as behavior based, structure based and spectral based. Each one of this classification further incorporates number of techniques which are discussed in the paper. The paper has been concluded with different future directions and areas of research that could be addressed and worked upon.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Anomaly detection in dynamic networks: a survey

Cited by 293 publications

References 127 publications

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations

A survey of data mining and social network analysis based anomaly detection techniques

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