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MNM@CASON 2014 : Multiplex network mining - special session : CASON'14

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Link: http://lipn.univ-paris13.fr/~kanawati/mnm14
 
When Jul 30, 2014 - Aug 1, 2014
Where Porto
Abstract Registration Due May 20, 2014
Submission Deadline May 31, 2014
Notification Due Jun 20, 2014
Final Version Due Jul 1, 2014
Categories    complex networks   data mining   social computing   machine learning
 

Call For Papers

Complex networks used to model interactions in real-world phenomena are often heterogeneous network with different types of nodes and edges. Focusing on a single type of nodes, a complex network would be better described by a multiplex: a set of nodes related to each other with different types of relations. This representation is much richer than simple complex networks often used to model complex interaction systems. However, this poses the challenge to provide adequate answers to all basic network analysis tasks that have been studied and provided in the recent few years for the case of homogeneous networks. This include for instance: the problem of node ranking (computing nodes centralities), community detection, link prediction, information diffusion models and network visualization. Almost all work in the field of multiplex network analysis are based on transforming the problem, in a way or another to the classical case of homogeneous network analysis. Existing approaches include: layer aggregation based approaches or applying ensemble methods on results obtained on each layer aside. Little work has focused on analyzing all layers at once. The goal of this session is make the point on new approaches for multiplex network mining.

A non-exhaustive list of relevant topics include:

- Models and measures for multiplex networks.
- Co-evolution of layers in multiplex networks.
- Layer aggregation approaches.
- Vertex similarity in multiplex network
- Community detection in multiplex network
- Link prediction in multiplex network
- Multiplex network evolution models
- Multiplex network and dynamic network mining
- Ensemble learning for multiplex network mining
- Applications of multiplex network mining and modeling.

Submitted papers should be original and contain contributions of theoretical, experimental or application nature, or be unique experience reports.

The page limit for a full-length paper is 6 pages. Short papers describing novel research visions, work-in-progress or less mature results are also welcome, with a minimum limit of 4 pages. All submissions should be in the IEEE 8.5 two-column format. Please refer to the author templates given below:

MS Word template: Doc Template
LaTex Formatting Macros: TeX Template

Submit your paper at the submission site:

https://www.easychair.org/conferences/?conf=cason2014

Papers will be evaluated for originality, significance, clarity, and soundness, and will be reviewed by at least three independent reviewers. The conference proceedings will be distributed amongst the participants during the conference. Accepted papers must be presented by author(s) personally to be published in the conference proceedings.



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