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Keywords
(4)
Anomaly Detection
Eigenvalues
Weighted Graph
Power Law
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Oddball: Spotting Anomalies in Weighted Graphs
Oddball: Spotting Anomalies in Weighted Graphs,10.1007/9783642136726_40,Leman Akoglu,Mary McGlohon,Christos Faloutsos
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Oddball: Spotting Anomalies in Weighted Graphs
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Citations: 8
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Leman Akoglu
,
Mary McGlohon
,
Christos Faloutsos
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the oddball algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and
eigenvalues
that seem to govern the socalled “neighborhood subgraphs” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design oddball, so that it is scalable and it can work unsupervised (no userdefined constants) and (c) we report experiments on many real graphs with up to 1.6 million nodes, where oddball indeed spots unusual nodes that agree with intuition.
Published in 2010.
DOI:
10.1007/9783642136726_40
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Citation Context
(6)
...A number of different methods for network outlier detection have been discussed in [
8
], [11], [13]...
Charu C. Aggarwal
,
et al.
Outlier detection in graph streams
...Unlike previous such work that consider (offline) anomaly detection in static network graphs (e.g., [
6
]), our methods can be implemented efficiently online, and take into account properties of the network that evolve over time...
Chrisil Arackaparambil
,
et al.
WikiWatchdog: Anomaly Detection in Wikipedia Through a Distributional...
...In [
3
], Akoglu proposed to model each entity as induced subgraph of its neighboring nodes, discovering power laws in density, weights, ranks and eigenvalues, so as to extract fastcomputing numerical features to detect anomaly in weighted graphs...
...Therefore most works take graph structures to model entities, such as [
3
] and [4], assuming that entities in the networks can be characterized by their indegree, outdegree, neighborhoods, etc...
...As in [
3
], we believe that not properties of entities themselves but anomalous interacting behaviors are the key s to detect abnormal entities...
Jun Chen
,
et al.
Modeling entities in interaction dataset for anomaly detection and exp...
...Approaches to anomaly detection on networked data have explored the use of minimum description length (MDL) principle [16], [5], [18], [9], classificationbased methods [15], probabilistic measures [10], spectral methods [13], [12], and neighborhoodbased metrics [20], [19], [
3
]...
James Abello
,
et al.
Detecting Novel Discrepancies in Communication Networks
...unexpected/missing nodes/edges). Chakrabarti [6] and Sun et al. [28] spot anomalous edges, using MDL and proximity scores, respectively. OddBall [
1
] focuses on anomalous nodes...
Koji Maruhashi
,
et al.
EigenDiagnostics: Spotting Connection Patterns and Outliers in Large G...
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Citations
(8)
Outlier detection in graph streams
(
Citations: 1
)
Charu C. Aggarwal
,
Yuchen Zhao
,
Philip S. Yu
Conference:
International Conference on Data Engineering  ICDE
, pp. 399409, 2011
MultiAspectForensics: Pattern Mining on LargeScale Heterogeneous Networks with Tensor Analysis
Koji Maruhashi
,
Fan Guo
,
Christos Faloutsos
Conference:
Advances in Social Network Analysis and Mining  ASONAM
, 2011
WikiWatchdog: Anomaly Detection in Wikipedia Through a Distributional Lens
Chrisil Arackaparambil
,
Guanhua Yan
Conference:
Web Intelligence  WI
, 2011
Modeling entities in interaction dataset for anomaly detection and explanation
Jun Chen
,
Qingsheng Zhu
Conference:
International Conference on Communication Software and Networks  ICCSN
, 2011
Metric forensics: a multilevel approach for mining volatile graphs
(
Citations: 1
)
Keith Henderson
,
Tina EliassiRad
,
Christos Faloutsos
,
Leman Akoglu
,
Lei Li
,
Koji Maruhashi
,
B. Aditya Prakash
,
Hanghang Tong
Conference:
Knowledge Discovery and Data Mining  KDD
, pp. 163172, 2010