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oddball: Spotting Anomalies in Weighted Graphs [chapter]

Leman Akoglu, Mary McGlohon, Christos Faloutsos
2010 Lecture Notes in Computer Science  
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.  ...  on many real graphs with up to 1.6 million nodes, where OddBall indeed spots unusual nodes that agree with intuition.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties.  ... 
doi:10.1007/978-3-642-13672-6_40 fatcat:xym6jah6lvbhxmvjbfkpjk2674

OddBall: Spotting Anomalies in Weighted Graphs

Leman Akoglu, Mary McGlohon, Christos Faloutsos
2018
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.  ...  on many real graphs with up to 1.6 millionnodes, where oddball indeed spots unusual nodes that agree with intuition.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties.  ... 
doi:10.1184/r1/6607802.v1 fatcat:zomcgai5g5fkjntns5qmz4wema

OPAvion

Leman Akoglu, Duen Horng Chau, U. Kang, Danai Koutra, Christos Faloutsos
2012 Proceedings of the 2012 international conference on Management of Data - SIGMOD '12  
distribution, triangles, etc.; (2) The Anomaly Detection module (OddBall) uses graph statistics to mine patterns and spot anomalies, such as nodes with many contacts but few interactions with them (possibly  ...  Given a large graph with millions or billions of nodes and edges, like a who-follows-whom Twitter graph, how do we scalably compute its statistics, summarize its patterns, spot anomalies, visualize and  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory  ... 
doi:10.1145/2213836.2213941 dblp:conf/sigmod/AkogluCKKF12 fatcat:oj7arrwbvzf33ivfphz2llib4e

BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection [article]

Yulin Zhu, Yuni Lai, Kaifa Zhao, Xiapu Luo, Mingquan Yuan, Jian Ren, Kai Zhou
2022 arXiv   pre-print
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques.  ...  Our comprehensive experiments demonstrate that BinarizedAttack is very effective in enabling target nodes to evade graph-based anomaly detection tools with limited attackers' budget, and in the black-box  ...  INTRODUCTION Anomaly detection is a long-standing task in the field of data science and engineering with the goal to spot unusual patterns from the massive amount of data.  ... 
arXiv:2106.09989v5 fatcat:gye6nrb46rce7gupsz5ds6yw34

Anomaly Detection in Graphs of Bank Transactions for Anti Money Laundering Applications

Bogdan Dumitrescu, Andra Baltoiu, Stefania Budulan
2022 IEEE Access  
Although we have a labeled real dataset, our target is not only to obtain relevant results on it, but also on random graphs in which typical anomaly patterns have been injected.  ...  Our aim in this paper is to detect bank clients involved in suspicious activities related to money laundering, using the graph of transactions of the bank.  ...  OddBall gives anomaly scores for nine pairs of statistics.  ... 
doi:10.1109/access.2022.3170467 fatcat:vbblqcmyczgmfop2blz6gt5i7a

Fraud Detection through Graph-Based User Behavior Modeling

Alex Beutel, Leman Akoglu, Christos Faloutsos
2015 Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security - CCS '15  
In this tutorial we will answer these questionsconnecting graph analysis tools for user behavior modeling to anomaly and fraud detection.  ...  In particular, we will focus on three data mining techniques: subgraph analysis, label propagation and latent factor models; and their application to static graphs, e.g. social networks, evolving graphs  ...  In ICWSM, 2013. [3] Leman Akoglu, Mary McGlohon, and Christos Faloutsos. Oddball: Spotting anomalies in weighted graphs. PAKDD 2010, 21-24 June 2010. [4] Reid Andersen, Fan Chung, and Kevin Lang.  ... 
doi:10.1145/2810103.2812702 dblp:conf/ccs/BeutelAF15 fatcat:yfgbw3wkkzcs3jgunwqpqma2t4

Metric forensics

Keith Henderson, Tina Eliassi-Rad, Christos Faloutsos, Leman Akoglu, Lei Li, Koji Maruhashi, B. Aditya Prakash, Hanghang Tong
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
The results demonstrate the scalability and capability of MetricForensics in analyzing volatile graphs; and highlight four novel phenomena in such graphs: elbows, broken correlations, prolonged spikes,  ...  Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis.  ...  Oddball computes the number of edges, the total weight of edges, etc and defines the vertices as points in a multi-dimensional feature space, in which it looks for anomalies.  ... 
doi:10.1145/1835804.1835828 dblp:conf/kdd/HendersonEFALMPT10 fatcat:l5p2r3353rc3pe2ffj5yam577y

Catching bad guys with graph mining

Polo Chau
2011 XRDS Crossroads The ACM Magazine for Students  
He builds interactive systems that help analysts explore and make sense of large graph data, find patterns, detect fraud, and spot anomalies.  ...  Oddball can detect several important patterns, such as near-cliques and near-stars (by correlating the total edge weight and total edge count in the egonet).  ... 
doi:10.1145/1925041.1925044 fatcat:y5q4ehz5xfbzloyc5eikmpfe3e

An Event-related Neuroimaging Study Distinguishing Form and Content in Sentence Processing

W. Ni, R. T. Constable, W. E. Mencl, K. R. Pugh, R. K. Fulbright, S. E. Shaywitz, B. A. Shaywitz, J. C. Gore, D. Shankweiler
2000 Journal of Cognitive Neuroscience  
Effects of syntactic and semantic anomalies were differentiated by some nonoverlapping areas of activation: Syntactic anomaly triggered significantly increased activity in and around Broca's area, whereas  ...  While in the scanner, 14 young, unimpaired adults listened to simple sentences that were either nonanomalous or contained a grammatical error (for example, *Trees can grew.), or a semantic anomaly (for  ...  We also thank Hedy Sarofin and Terry Hickey for their help in imaging the subjects. Reprint requests should be sent to Weijia Ni, PhD, Haskins Laboratories, 270 Crown St., New Haven, CT 06510,  ... 
doi:10.1162/08989290051137648 pmid:10769310 fatcat:uk7sfgamaneibi6bhgfqyq42wa

Scalable Anomaly Ranking of Attributed Neighborhoods [article]

Bryan Perozzi, Leman Akoglu
2016 arXiv   pre-print
Experiments on real-world attributed graphs illustrate the effectiveness of our measure at anomaly detection, outperforming popular approaches including conductance, density, OddBall, and SODA.  ...  In addition to anomaly detection, our qualitative analysis demonstrates the utility of normality as a powerful tool to contrast the correlation between structure and attributes across different graphs.  ...  Specifically, we show the utility of our measure in spotting anomalies in attributed graphs, where AMEN outperforms existing approaches including conductance, density, OddBall [1] , and SODA [11] by  ... 
arXiv:1601.06711v1 fatcat:5xyszz55rnh75kznzpf56hti7u

Scalable Anomaly Ranking of Attributed Neighborhoods

Bryan Perozzi, Leman Akoglu
2016 Proceedings of the 2016 SIAM International Conference on Data Mining  
Experiments on real-world attributed graphs illustrate the effectiveness of our measure at anomaly detection, outperforming popular approaches including conductance, density, OddBall, and SODA.  ...  In addition to anomaly detection, our qualitative analysis demonstrates the utility of normality as a powerful tool to contrast the correlation between structure and attributes across different graphs.  ...  Specifically, we show the utility of our measure in spotting anomalies in attributed graphs, where AMEN outperforms existing approaches including conductance, density, OddBall [1] , and SODA [11] by  ... 
doi:10.1137/1.9781611974348.24 dblp:conf/sdm/PerozziA16 fatcat:k3wcv4hqdzdltmlb2ovs44ojde

SOME OBSERVATION OF ALGORITHMS DEVELOPED FOR ANOMALY DETECTION

Pallavi Raj, Rakhi Garg
2020 Indian Journal of Computer Science and Engineering  
This paper mainly focuses on the graph mining techniques used for anomaly detection in social networks.  ...  The algorithm for anomaly detection using graph mining techniques has been categorized on the basis of different characteristics of anomalies, and the types of anomalies generated.  ...  Algorithms for anomaly detection in dynamic unlabeled graph In dynamic unlabeled graph, anomalies are detected by considering the changes occur in the structure of the network.  ... 
doi:10.21817/indjcse/2020/v11i1/201101005 fatcat:ynz45figozbu7fuznqo2u3j55y

A Community-Aware Approach for Identifying Node Anomalies in Complex Networks [chapter]

Thomas J. Helling, Johannes C. Scholtes, Frank W. Takes
2018 Studies in Computational Intelligence  
A community-aware approach for identifying node anomalies in complex networks Helling, T.J.; Scholtes, J.C.; Takes, F.W. Abstract.  ...  Moreover, the proposed method is parameter-free, enabling the hassle-free identification of anomalies in a wide variety of application domains.  ...  Typically, three types of anomalies in static networks can be distinguished: node anomalies, edge anomalies, and sub-graph anomalies [2, 4] .  ... 
doi:10.1007/978-3-030-05411-3_20 fatcat:xptbtlanoraotipn4u3tgiwdxq

Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach [article]

Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos
2020 arXiv   pre-print
We show theoretically and experimentally that the two-pronged approach successfully detects two common types of anomalies: sudden weight changes along an edge, and sudden structural changes to the graph  ...  In this work, we propose AnomRank, an online algorithm for anomaly detection in dynamic graphs. AnomRank uses a two-pronged approach defining two novel metrics for anomalousness.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding  ... 
arXiv:2011.13085v1 fatcat:27h5knea5bhy7ah2kv24n22ufq

CatchSync

Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
Given a directed graph of millions of nodes, how can we automatically spot anomalous, suspicious nodes, judging only from their connectivity patterns?  ...  competitors, both in detection accuracy by 36% on Twitter and 20% on Tencent Weibo, as well as in speed.  ...  ACKNOWLEDGEMENT Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation  ... 
doi:10.1145/2623330.2623632 dblp:conf/kdd/JiangCBFY14 fatcat:4xcbe2s46zcl7a7swdbk2to42e
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