Outlier detection in graph streams

Aggarwal, Charų C.; Zhao, Yuchen; Yu, Philip S.

doi:10.1109/icde.2011.5767885

Cited by 177 publications

(113 citation statements)

References 23 publications

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“…The problem studied in this paper has connections with distance-based outlier detection algorithms [21] in the sense that we are trying to search outliers in a space with community change trends as dimensions. Outliers have been discovered in highdimensional data [2], uncertain data [3], stream data [4], network data [15] and time series data [13]. Recently, there has been significant interest in detecting outliers in evolving datasets [16,17], but none of them explores the outliers with respect to communities in a general evolving dataset.…”

Section: Related Workmentioning

confidence: 99%

“…(2) Temporal outlier detection: Traditional time series literature [13] defines two types of outliers (Type I/additive and Type II/innovative) based on the data associated with an individual object across time, ignoring the community aspect completely. (3) Stream outlier detection: Recent work on outlier detection on data streams has focused on distancebased local outliers [29] or on graph outliers [4], while we focus on outliers in the community context. In general, existing work ignores time or community information in outlier detection, and thus the outliers detected traditionally are not evolutionary community outliers as proposed in this paper.…”

Section: Existing Workmentioning

confidence: 99%

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Integrating community matching and outlier detection for mining evolutionary community outliers

Gupta

Gao²,

Sun

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Temporal datasets, in which data evolves continuously, exist in a wide variety of applications, and identifying anomalous or outlying objects from temporal datasets is an important and challenging task. Different from traditional outlier detection, which detects objects that have quite different behavior compared with the other objects, temporal outlier detection tries to identify objects that have different evolutionary behavior compared with other objects. Usually objects form multiple communities, and most of the objects belonging to the same community follow similar patterns of evolution. However, there are some objects which evolve in a very different way relative to other community members, and we define such objects as evolutionary community outliers. This definition represents a novel type of outliers considering both temporal dimension and community patterns. We investigate the problem of identifying evolutionary community outliers given the discovered communities from two snapshots of an evolving dataset. To tackle the challenges of community evolution and outlier detection, we propose an integrated optimization framework which conducts outlier-aware community matching across snapshots and identification of evolutionary outliers in a tightly coupled way. A coordinate descent algorithm is proposed to improve community matching and outlier detection performance iteratively. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting evolutionary community outliers.

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Section: Related Workmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

Integrating community matching and outlier detection for mining evolutionary community outliers

Gupta

Gao²,

Sun

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…We are able to achieve a higher AUC than the baseline in addition to providing constant space and time complexities (Appendix B). No time or space complexity analysis for the baseline is shown in [4].…”

Section: Approximation Effects On Precisionmentioning

confidence: 99%

“…As a baseline, we implemented the graph stream outlier detection approach proposed in [4]; see Section 5 for more details of this approach. Similar to our edge score, each edge in the baseline is assigned a probability of appearing.…”

Section: Methodsmentioning

confidence: 99%

“…In [4], the authors introduce a structural reservoir sampling technique to heuristically store sub-structures in the graph to facilitate analysis on multiple samples of the graph. We propose a complementary method that uses a probabilistic data structure, known as the Count Min (CM) sketch, to store an approximation of the complete stream.…”

Section: Model Approximationmentioning

confidence: 99%

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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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Anomaly detection in dynamic networks: a survey

Ranshous

Shen

Koutra

et al. 2015

WIREs Computational Stats

307

191

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Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the state-of-the-art methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general two-stage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a two-tiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categories-community, compression, decomposition, distance, and probabilistic model based-we highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches.

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Outlier detection in graph streams

Cited by 177 publications

References 23 publications

Integrating community matching and outlier detection for mining evolutionary community outliers

Integrating community matching and outlier detection for mining evolutionary community outliers

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

Anomaly detection in dynamic networks: a survey

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