MACHINE LEARNING JOURNAL
Call for papers: Special issue on Dynamic Networks and Knowledge Discovery

Modeling and analyzing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval, etc. Nowadays, the scientific communities have access to huge volumes of network-structured data, such as social networks, gene/proteins/metabolic networks, sensor networks, peer-to-peer networks. Most often, these data are dynamic, and a dynamic view of the system allows the time component to play a key role in the comprehension of the evolutionary behavior of the network.
Handling such data is a major challenge for current research in machine learning and data mining. It has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs, heterogeneous information (nodes and links), and requires scalable algorithms that are able to manage huge and complex networks. The special issue, after the second DYNAK workshop that recently took place at ECML/PKDD 2014 aims at attracting contributions from both aspects of networks analysis: large real network analysis and modeling and knowledge discovery within those networks.
Authors are invited to submit previously unpublished papers, as well as substantially extended versions of papers accepted to the DYNAK workshop or recent Machine Learning and Data Mining major conferences. We are interested in theoretical research and/or applications in any topic related to one or more of the workshop topics. A non-exhaustive list of topics is given hereafter:


Methods:
• Network inference from raw data
• Graphical models
• Graph mining algorithms
• Graph kernel algorithms
• Relational learning algorithms
• Matrix/Tensor methods
• Information retrieval algorithms
• Bayesian methods
• Evolutionary clustering
• Mining heterogenous networks
• Multiplex network analysis & mining
• Bisociative information discovery
• Clustering/Co-clustering/Biclustering
• Pattern mining with constraints
• Community detection
• Social & biological networks analogy

Applications:
• Recommender systems
• System biology: regulatory gene networks, protein-protein interaction, miRNA networks, metabolic networks
• Social networks: folksonomies, digital libraries, information networks, social media, collaborative networks
• Sensor networks, peer-to-peer networks, Web, agent networks, body sensor networks


SUBMISSION

Authors are invited to submit an abstract (2 pages maximum, including major references, see http://lipn.fr/mlj-si for details) before submitting a full paper. Abstract will be selected at that stage only on the basis of their relevance to the call. Authors of selected abstracts will be invited to submit a full paper.
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. All papers will be reviewed following standard reviewing procedures for the Journal. Papers must be prepared in accordance with the Journal guidelines: http://www.springer.com/10994. Instructions for Authors and LaTeX style files can also be found at this site. Manuscripts must be submitted to: http://MACH.edmgr.com. Choose “Dynamic Networks and Knowledge Discovery” as the article type.

SCHEDULE

7-Jan-2015 : Abstract due
16-Jan-2015 : Feedback on abstracts (go/no go)
01-Mar-2015 : Submission deadline
15-Jun-2015 : First notification
15-Sep-2015 : Final version due

EDITORS

Rushed Kanawati, University of Paris Nord, France
(rushed.kanawati@lipn.univ-paris13.fr)
Ruggero G. Pensa, University of Torino, Italy
(mailto:pensa@di.unito.it)
Céline Rouveirol, University of Paris Nord, France (celine.rouveirol@lipn.univ-paris13.fr)