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marami@lipn.univ-paris13.fr

June 2018

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[CFP : Special issue Applied Network Science : Modeling, Analyzing and Mining Feature-Rich Networks ]
by Rushed Kanawati 08 Jun '18

08 Jun '18

Modeling, Analyzing and Mining Feature-Rich Networks Special Issue for Applied Network Science Deadlines: Expression of interest: September 21, 2018 Full paper submission: October 28, 2018. The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations. The design of innovative complex approaches is the key to enable a deeper understanding of feature-rich networks, i.e., network models exposing specific features able to enhance their expressive power. Some examples of feature-rich networks are Multilayer Networks, Temporal and Heterogeneous Networks, Knowledge Graphs, Probabilistic Networks and generic Attributed Graphs. In this scenario, the need to expose and process domain-specific features when facing critical real-world tasks can prompt researchers to exploit the full potential of mining complex network structures. The aim of this Special Issue is to provide an insight into innovative methods to model, analyze and mine feature-rich networks inspired from different fields, incentivizing domain-driven approaches that can drive the design of novel network models. You are invited to submit a contribution (regular manuscripts or review article). Topics of interest for this special issue include (but are not limited to): - Centrality and Ranking in Feature-rich Networks - Community Detection in Feature-rich Networks - Cross domain problems in Feature-rich Networks - Link Prediction in Feature-rich Networks - Probabilistic and Uncertain Feature-rich Networks - Pattern mining in Feature-rich Networks - Personalization and Recommendation in Feature-rich Networks - Representation Learning in Feature-rich Networks - Reputation and Trust in Feature-rich Networks - Sampling/Pruning/Simplification of Feature-rich Networks - Social Influence and Information Diffusion in Feature-rich Networks - Time-evolving Feature-rich Networks - User Behavior Modeling in Feature-rich Networks - Visualization of Feature-rich Networks Guest editors: Martin Atzmueller Tilburg University , Netherlands m.atzmuller(a)uvt.nl <mailto:m.atzmuller@uvt.nl> Sabrina Gaito University of Milano, Italy gaito(a)di.unimi.it <mailto:gaito@di.unimi.it> Roberto Interdonato CIRAD, France roberto.interdonato(a)cirad.fr <mailto:roberto.interdonato@cirad.fr> Rushed Kanawati Paris 13 University, France rushed.kanawati(a)lipn.univ-paris13.fr <mailto:rushed.kanawati@lipn.univ-paris13.fr> Christine Largeron University of Lyon, France christine.largeron(a)univ-st-etienne.fr <mailto:christine.largeron@univ-st-etienne.fr> Alessandra Sala, Nokia Bell Labs, Ireland alessandra.sala(a)nokia-bell-labs.com <mailto:alessandra.sala@nokia-bell-labs.com> Submission timeline - Expression of interest: September 21, 2018 We invite authors to submit a brief expression of interest and a short outline or extended abstract (approx. 1000 words). The proposal should include: o The topic, o Key concepts, o Methods (if the paper is based on empirical research), o Expected results and conclusions. Please send it by e-mail to the Lead guest editor: Roberto Interdonato, roberto.interdonato(a)cirad.fr <mailto:roberto.interdonato@cirad.fr> - Feedback on outline / extended abstract: September 28, 2018 - Full paper submission deadline: October 28, 2018. Full submission guidelines can be found at the Applied Network science website. https://appliednetsci.springeropen.com/submission-guidelines <https://appliednetsci.springeropen.com/submission-guidelines> All manuscripts should be submitted through the Applied Network Science submission system: https://www.editorialmanager.com/apns/default.aspx <https://www.editorialmanager.com/apns/default.aspx> Target publication date: February 28, 2019 Papers will be subject to a fast track review procedure that will start as soon as they are submitted. They will be published as soon as they are accepted. For more information, please direct your questions to the Lead guest editor: Roberto Interdonato roberto.interdonato(a)cirad.fr <mailto:roberto.interdonato@cirad.fr> _________________________________________________ R. Kanawati Chef du département R&T - IUT de Villetaneuse 99 Av. J. B. Clément 93430 Villetaneuse O218 - Tél. 0149402896

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