MLJ 2015 - Machine Learning Journal Special issue on Dynamic Networks & 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 ne twork.
Handling such data is a major chall enge 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. This special issue, organized 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 Authors are invited to submit previously unpublished papers, as well as substantially extended versions of papers accepted to the DYNAK workshop or recent Machine Learning and Data Mining major conferences.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 netwo rks, Social media, collaborative networks
- Sensor networks: P2P networks, Web, 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. The submission of abstracts is to be done through the easychair submission web site :
https://www.easychair.org/conferences/?conf=mlj-dy...
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. 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.
Important dates:
07/01/2015 Abstract due
16/01/2015 Feedback on abstracts (go. no go)
01/03/2015 Submission deadline
15/06/2015 First notification.
Guest editors:
Rushed Kanawati, SPC, University of Paris 13, France
Ruggero G. Pensa, University of Torino, Italy
Céline Rouveirol, SPC, University of Paris 13, France