[Liste-proml] PHD position at Hubert Curien Laboratory Saint-Etienne

Christine Largeron Christine.Largeron at univ-st-etienne.fr
Ven 30 Mar 16:37:18 CEST 2018


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*Title*
Anomaly detection in dynamic attributed networks with community structure.

*Laboratory:*Laboratoire Hubert Curien - University of Saint Etienne
https://laboratoirehubertcurien.univ-st-etienne.fr

*Advisors*
Christine Largeron largeron at univ-st-etienne.fr
Baptiste Jeudy baptiste.jeudy at univ-st-etienne.fr.

*Context *
In the recent few years, network data has become ubiquitous and has 
attracted great interest in the data mining community. A variety of 
methods and software solutions have been proposed to ease their 
analysis. On the other hand, anomaly detection is an important problem 
in many application domains.
Thus anomaly detection in graphs, and in particular in dynamic graphs 
(graphs which change over time) is a hot topic and the subject of recent 
research papers [Ranshous2015, Eberle2007, Noble2003, Akoglu2014, 
Gupta2014].
Anomaly detection in dynamic networks deals with the problem of finding 
nodes, edges, points in time or substructures that are dissimilar with 
respect to the rest of the network. Applications includes discovery of 
extreme physical events (cyclones) [Chen2013], intrusion detection 
[Ding2012], communication networks analysis [Priebe2005],
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Subject *
In this thesis, we propose to study anomaly detection in dynamic 
attributed networks with community structure and to focus on detecting 
anomalous nodes. We propose to do this using both the relationship 
between nodes (the graph) and the attributes. More precisely, a node 
could be considered as anomalous if it presents atypical attribute 
values given its community membership. This kind of approach has never 
been tried to our knowledge.

*Working environment*
The PhD candidate will work at Laboratoire Hubert Curien (University of 
Saint Etienne, https://laboratoirehubertcurien.univ-st-etienne.fr) in 
the Data Intelligence team. This team has an expertise in social 
networks, deep learning, pattern mining and anomaly detection among others.
The two supervisors for this PhD thesis are:
* Christine Largeron, Professor in Computer Science since 2006. Her 
current research focuses on social mining and text mining.
* Baptiste Jeudy, Associate Professor in Computer Science since 
September 2006.His research topics are data mining, in particular in 
graphs and graphs sequences.

*Funding* the Ph.D. fellowship is funded for 3 years and is monthly 
funded about approximatively 1450 €.

*Profile of the candidate*
The candidate should have a master degree or equivalent in computer 
science. The subject is at the intersection of several domains: graph 
theory (applied to social network and community detection), anomaly 
detection (statistics and machine learning) and big data (the considered 
networks can be huge). Thus the candidate should have strong backgrounds 
in several of these topics.
Other required skills :

  * Good abilities in algorithm design and programming ;
  * a very good level (written and oral) in English ;
  * good communication skills (oral and written);
  * autonomy and motivation for research.


*Application instructions*
Send your application with a CV, your last grade certificate (if you are 
currently finishing your Master’s degree, we need an official list of 
the grades you obtained so far in this degree with your rank among your 
peers), some recommendation letters and a specific motivation letter to 
largeron at univ-st-etienne.fr and baptiste.jeudy at univ-st-etienne.fr.
The application is opened until the 24^th April. Some interviews will be 
offered between the 25^th April and the 4^th May.
The final decision will be given in June. The PhD thesis is expected to 
start in September (or October) 2018.
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