PhD Defense: Analyse exploratoire de flots de liens pour la détection d'événe...Sébastien
Link streams represent traces of complex systems’ activities over time, in which links appear when two system entities interact with each other; the aggregation of entities (i.e. nodes) and links is a graph. These traces have become strategic datasets in the last few years for analyzing the activity of large-scale complex systems, involving millions of entities, e.g. mobile phone networks, social networks, or the Internet.
This thesis deals with the exploratory analysis of link streams, in particular the characterization of their dynamics and the identification of anomalies over time (called events). We propose an exploratory framework involving statistical methods and visualization, with no hypothesis about data. The detected events are statistically significant and we propose a method to validate their relevance. We finally illustrate our methodology on the evolution of Github online social network, on which hundred thousands of developers contribute to open source software projects.
COMMUNITY DETECTION IN THE COLLABORATIVE WEBIJMIT JOURNAL
Most of the existing social network systems require from their users an explicit statement of their friendship relations. In this paper we focus on implicit Web communities and present an approach to automatically detect them, based on user’s resource manipulations. This approach is dynamic as user
groups appear and evolve along with users interests over time. Moreover, new resources are dynamically labelled according to who is manipulating them. Our proposal relies on the fuzzy K-means clustering method and is assessed on large movie datasets.
Predictions of links in graphs based on content and information propagations.
Lecture for the M. Sc. Data Science, Sapienza University of Rome, Spring 2016.
PhD Defense: Analyse exploratoire de flots de liens pour la détection d'événe...Sébastien
Link streams represent traces of complex systems’ activities over time, in which links appear when two system entities interact with each other; the aggregation of entities (i.e. nodes) and links is a graph. These traces have become strategic datasets in the last few years for analyzing the activity of large-scale complex systems, involving millions of entities, e.g. mobile phone networks, social networks, or the Internet.
This thesis deals with the exploratory analysis of link streams, in particular the characterization of their dynamics and the identification of anomalies over time (called events). We propose an exploratory framework involving statistical methods and visualization, with no hypothesis about data. The detected events are statistically significant and we propose a method to validate their relevance. We finally illustrate our methodology on the evolution of Github online social network, on which hundred thousands of developers contribute to open source software projects.
COMMUNITY DETECTION IN THE COLLABORATIVE WEBIJMIT JOURNAL
Most of the existing social network systems require from their users an explicit statement of their friendship relations. In this paper we focus on implicit Web communities and present an approach to automatically detect them, based on user’s resource manipulations. This approach is dynamic as user
groups appear and evolve along with users interests over time. Moreover, new resources are dynamically labelled according to who is manipulating them. Our proposal relies on the fuzzy K-means clustering method and is assessed on large movie datasets.
Predictions of links in graphs based on content and information propagations.
Lecture for the M. Sc. Data Science, Sapienza University of Rome, Spring 2016.
From http://www.csdn.net/article/2015-12-17/2826501
《新加坡管理大学信息系统学院教授朱飞达 :大数据与金融创新:从研究到实战》
新加坡管理大学信息系统学院教授朱飞达分享了基于社交媒体大数据的个人征信应用模式,包括四个方面:提取社交维度特征,加入现在传统信用模型;采用产生式模式挖掘不同信用类别的隐含用户模型;基于社会关系网络的风险传递查询和探索引擎;实时反欺诈侦测和预警系统。
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Inferring Peer Centrality in Socially-Informed Peer-to-Peer SystemsNicolas Kourtellis
Social applications implemented on a peer-to-peer (P2P) architecture mine the social graph of their users for improved performance in search, recommendations, resource
sharing and others. In such applications, the social graph that connects their users is distributed on the peer-to-peer system: the traversal of the social graph translates to a socially-informed routing in the peer-to-peer layer.
In this work we introduce the model of a projection graph that is the result of mapping a social graph onto a peer-to-peer network. We analytically formulate the relation between metrics in the social graph and in the projection graph. We focus on three such graph metrics: degree centrality, node betweenness centrality, and edge betweenness centrality. We evaluate experimentally the feasibility of estimating these metrics in the projection graph from the metrics of the social graph. Our experiments on real networks show that when mapping communities of 50-150 users on a peer, there is an optimal organization of the projection graph with respect to degree and node betweenness centrality. In this range, the association between the properties of the social graph and the projection graph is the highest, and thus the properties of the (dynamic) projection graph can be inferred from
the properties of the (slower changing) social graph. We discuss the applicability of our findings to aspects of peer-to-peer systems such as data dissemination, social search, peer vulnerability, and data placement and caching.
Inferring Peer Centrality in Socially-Informed Peer-to-Peer Systems. Nicolas Kourtellis and Adriana Iamnitchi. In Proceedings of 11th IEEE International Conference on Peer-to-Peer Computing (P2P'11), Kyoto, Japan, Aug 2011
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Title: Detecting Potential Biases in Sequential Hand Gesture Recognition
This slide deck showcases my master's thesis, delving into the exploration of potential biases in sequential hand gesture recognition. The implemented model, utilizing CNN with VGG16 architecture, achieved an impressive 99.97% accuracy. The analytical framework was constructed using the iNNvestigate toolbox, employing Layerwise Relevance Propagation (LRP). To further interpret the LRP results, I incorporated agglomerative clustering through the Clustimage library.
For more in-depth information, feel free to connect with me on LinkedIn.
Shamim Miroliaei
Evolving social data mining and affective analysis Athena Vakali
Evolving social data mining and affective analysis methodologies, framework and applications - Web 2.0 facts and social data
Social associations and all kinds of graphs
Evolving social data mining
Emotion-aware social data analysis
Frameworks and Applications
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
An adaptive gmm approach to background subtraction for application in real ti...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
From http://www.csdn.net/article/2015-12-17/2826501
《新加坡管理大学信息系统学院教授朱飞达 :大数据与金融创新:从研究到实战》
新加坡管理大学信息系统学院教授朱飞达分享了基于社交媒体大数据的个人征信应用模式,包括四个方面:提取社交维度特征,加入现在传统信用模型;采用产生式模式挖掘不同信用类别的隐含用户模型;基于社会关系网络的风险传递查询和探索引擎;实时反欺诈侦测和预警系统。
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Inferring Peer Centrality in Socially-Informed Peer-to-Peer SystemsNicolas Kourtellis
Social applications implemented on a peer-to-peer (P2P) architecture mine the social graph of their users for improved performance in search, recommendations, resource
sharing and others. In such applications, the social graph that connects their users is distributed on the peer-to-peer system: the traversal of the social graph translates to a socially-informed routing in the peer-to-peer layer.
In this work we introduce the model of a projection graph that is the result of mapping a social graph onto a peer-to-peer network. We analytically formulate the relation between metrics in the social graph and in the projection graph. We focus on three such graph metrics: degree centrality, node betweenness centrality, and edge betweenness centrality. We evaluate experimentally the feasibility of estimating these metrics in the projection graph from the metrics of the social graph. Our experiments on real networks show that when mapping communities of 50-150 users on a peer, there is an optimal organization of the projection graph with respect to degree and node betweenness centrality. In this range, the association between the properties of the social graph and the projection graph is the highest, and thus the properties of the (dynamic) projection graph can be inferred from
the properties of the (slower changing) social graph. We discuss the applicability of our findings to aspects of peer-to-peer systems such as data dissemination, social search, peer vulnerability, and data placement and caching.
Inferring Peer Centrality in Socially-Informed Peer-to-Peer Systems. Nicolas Kourtellis and Adriana Iamnitchi. In Proceedings of 11th IEEE International Conference on Peer-to-Peer Computing (P2P'11), Kyoto, Japan, Aug 2011
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Title: Detecting Potential Biases in Sequential Hand Gesture Recognition
This slide deck showcases my master's thesis, delving into the exploration of potential biases in sequential hand gesture recognition. The implemented model, utilizing CNN with VGG16 architecture, achieved an impressive 99.97% accuracy. The analytical framework was constructed using the iNNvestigate toolbox, employing Layerwise Relevance Propagation (LRP). To further interpret the LRP results, I incorporated agglomerative clustering through the Clustimage library.
For more in-depth information, feel free to connect with me on LinkedIn.
Shamim Miroliaei
Evolving social data mining and affective analysis Athena Vakali
Evolving social data mining and affective analysis methodologies, framework and applications - Web 2.0 facts and social data
Social associations and all kinds of graphs
Evolving social data mining
Emotion-aware social data analysis
Frameworks and Applications
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
An adaptive gmm approach to background subtraction for application in real ti...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Similar to Monitoring User-System Interactions through Graph-Based Intrinsic Dynamics Analysis (20)
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Most of Free/Open Source Software (FOSS) developers are not paid to contribute, so why do they work anyway? In this talk, we’ll investigate the motivations of individual contributors. We’ll put them in perspective with recent studies on motivations and communities of practice. In particular, we’ll see that distinguishing internal vs external incentives is a key to understand why FOSS communities are able to attract and keep contributors around the production of a software…
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Monitoring User-System Interactions through Graph-Based Intrinsic Dynamics Analysis
1. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Monitoring User-System
Interactions through Graph-Based
Intrinsic Dynamics Analysis
S´ebastien Heymann, B´en´edicte Le Grand
Emails: Sebastien.Heymann@lip6.fr, Benedicte.Le-Grand@univ-paris1.fr
May 30, 2013
2. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Monitoring user-system
interactions
What type of user-system interactions?
• user-invoked services in information systems
• social networks
• ...
What kind of monitoring?
• discovery
• conformance
• model improvement
Our ultimate goal: automatic and real-time anomaly detection.
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
2/28
3. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Studied social network
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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4. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Github interaction: code commit
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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5. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Github interaction: bug report
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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6. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Collected Dataset
👤 👤 👤
📸 📸 📸 📸 📸 📸
❞ ❞ 🎔
Interactions examples
commit code / merge
repositories.
open / close bug reports.
❞comment on bug reports.
🎔edit the repository wiki.
”who contributes to which source code repository”
• 336 000 users and repositories monitored during 4 months.
• 2.2 million interactions recorded sequentially with timestamps.
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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7. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Log trace sample
User, user, repository, event, timestamp
lukearmstrong, fuel, core, IssuesEvent, 1341420003
Try-Git, clarkeash, try git, CreateEvent, 1341420006
uGoMobi, jquery, jquery-mobile, IssuesEvent, 1341420009
jexp, neo4j, java-rest-binding, IssueCommentEvent, 1341420011
HosipLan, nette, nette, PullRequestEvent, 1341420152
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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8. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Bipartite graph
👤 👤 👤
📸 📸 📸 📸 📸 📸
: users
⊥: repositories
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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9. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
👤
📸
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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10. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
👤 👤
📸 📸
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
9/28
11. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
👤 👤
📸 📸 📸
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
9/28
12. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
👤 👤
📸 📸 📸 📸
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
9/28
13. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
👤 👤
📸 📸 📸 📸
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
9/28
14. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
👤 👤👤
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S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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15. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
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S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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16. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Links appear over time
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Detection of statistically abnormal links dynamics?
Model of links dynamics?
Link prediction?
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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17. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Methodology
1 Order links by timestamp.
2 Define a sliding window of width w (time unit?).
3 Extract the bipartite graph from each window at interval i.
4 Compute an appropriate property on each graph.
5 Analyze the time series.
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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18. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Example
Date
Nbnodes
500
1000
1500
11 March 13 April 31 May 18 July
weekly patternNumber of nodes
Date
Nbnodes
400
600
800
1000
1200
1400
1600
15 April 22 April
day-night pattern
zoom
w =1 hour, i = 5 minutes.
Question: don’t temporal patterns hide information?
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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19. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Notions of time
Extrinsic time (real time)
Time measured in units such as seconds.
Good at revealing exogenous phenomena, e.g. day-night patterns.
Intrinsic time (related to graph dynamics)
Time measured in units such as the transition of two states in the
graph.
Better at revealing endogenous phenomena independently from the
graph dynamics?
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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20. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Window width: high resolution
Time (nb links)
Nbnodes
200
400
600
800
1000
1200
500000 1000000 1500000 2000000
Number of nodes
w = 1000 links, i = 100 links.
:) Additional observation
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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21. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Window width: lower resolution
Number of nodes
Time (nb links)
Nbnodes
15000
20000
25000
30000
500000 1000000 1500000 2000000
w = 50, 000 links, i = 1000 links.
:) No need for high resolution
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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22. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Event validation
Visualization of the sub-graph: connected nodes are closer,
disconnected nodes are more distant.
In the sub-graph of
8,370 nodes and
10,000 links at the
time of the event,
one node has a high
number of links:
Try-Git interacts with
4,127 users (over
5,000).
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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23. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
http://try.github.io
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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24. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Towards automatic anomaly
detection
Need for more elaborate properties, like:
Internal links
Their removal does not change the projection of the graph for a
given set of nodes, either or ⊥.
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G G’ = G - (red link) G’T
= GT
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S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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25. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Results
Ratio of -internal links
Time (nb links)
Ratiooftop−internallinks
0.5
0.6
0.7
0.8
0.9
1.0
0 500000 1000000 1500000 2000000 2300000
not outlier potential outlier outlier unknown
A
B C
D
E
F
G
H I
J
K
w = 10, 000 links, i = 1000 links.
Color = outlier class using the automatic Outskewer method*.
* S. Heymann, M.Latapy and C. Magnien. Outskewer: Using Skewness to Spot
Outliers in Samples and Time Series, IEEE ASONAM 2012
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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26. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Conclusion
Contributions
• Graph-based methodology to monitor user-system interactions
• Intrinsic time unit avoids exogeneous patterns impact
• Smaller windows not necessarily optimal
• Checked relevance of detected events
Applicable in other contexts
• Client-server architectures
• Processes-messages graphs
• File-provider graphs
• User-invoked services in information systems
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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27. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Future work
• Which property for anomaly detection?
• Models of interaction dynamics
• Link prediction
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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31. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Statistically significant anomalies
General definition
Values which deviate remarkably from the remainder of values
(Grubbs, 1969)
Outskewer method*:
Our definition
Extremal value which skews a distribution of values.
* Heymann, Latapy and Magnien. Outskewer: Using Skewness to Spot Outliers in Samples and Time Series, IEEE
ASONAM 2012
S´ebastien Heymann, B´en´edicte Le Grand — Monitoring User-System Interactions — May 30, 2013
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32. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Skewness coefficient
γ = n
(n−1)(n−2) x∈X
x−mean
standard deviation
3
density
x
density
xγ < 0
γ > 0
Example of skewed distributions.
It is sensitive to extremal values (min/max) far from the mean !
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33. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Automatic anomaly detection
Outskewer classifies each value as:
qqqqqqqqqqqqq
qqqqqqqqqqq
2000
status
q not outlier
potential outlier
outlier
or ’unknown’ for heterogeneous distributions of values.
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34. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Event detection in time series
On a sliding window of size w, each value of X is classified w
times.
The final class of a value is the one that appears the most.
time
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35. l i p 6 u n i v e r s i t ´e d e p a r i s 1 - c r i
Why Outskewer?
• claims no strong hypothesis on data
• 1 parameter: the time window width
• ignores regime changes (shifts in normality)
• can be implemented on-line.
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