Foreword

Research in modeling, analyzing and mining large-scale networks has attracted
an increasing effort in the last few years. Two main reasons, at least, may
explain the rapid growth of interest in this field, as attested by the increasing
number of scientific publications about this topic:

   • On one hand, many datasets studied in various different fields are best
     described by graphs or linked collection of interrelated objects. Exam-
     ples cover a wide variety of application fields including: biological system
     studies, (protein interaction, gene/miRNA regulation, . . .,) the world wide
     web, bibliographical networks (co-authoring, citation, . . .), P2P networks,
     semantic networks, and of course the now very popular on-line social
     networking and microblogging sites (e.g., Facebook, Twitter, Google+),
     folksonomy-oriented sites (e.g., Foursquare, Delicious, Flickr) and social
     media platforms (e.g., YouTube, last.fm). Far beyond sharing a networked
     structure, many of these naturally arising graphs share some non-trivial
     features (such as power-law node’s degree distribution, small separation
     degree, high clustering coefficient, low density, . . ., etc). This fact has
     boosted the research in analyzing and mining this class of networks since
     findings in one field are expected to be easily applied to other analogue
     fields.

   • On the other hand, recent technological advances, in different areas, al-
     low today generating, elaborating and tracking the spatial and tempo-
     ral evolution of very large scale networks. For example, in systems biol-
     ogy, continuous improvement of technologies has enabled to provide high-
     throughput and heterogeneous datasets (genomic, proteomic, transcrip-
     tomic and metabolomic) allowing to construct huge networks with both
     rich node and edge meta-data. The possibility of repeating the same ex-
     periment at different time points allows to track the evolution of obtained
     networks, opening the way for understanding the causal relationships be-
     tween nodes and how these interactions change over time. Purchase data
     collected on e-commerce sites allow to build very large scale networks con-
     necting customers to products they bought. Again, analyzing and mining
     such networks would provide new directions for product recommendation
     computation. On-line social network sites connecting millions of users

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     and publicly available bibliographical databases featuring millions of en-
     tries are some examples where a temporal sequence of large-scale networks
     can be sampled. 107 nodes size networks are no more an exception. The
     spatial evolution of social phenomena is another promising field of re-
     search. For instance, investigating how memes diffuse geographically may
     support the validation or even the discovery of new important sociological
     hypotheses.
    The second edition of our Workshop on Dynamic Networks and Knowledge
Discovery has received 15 submissions: 8 were only accepted as long presenta-
tions. These are organized into three main sessions:

Application session: This contains two papers. The first one by Shijaku et.al.
    introducing the concept of dynamic embeddedness with an application to
    the analysis of global pharmaceutical industry interaction network. The
    second paper is proposed by Correa and Alves, in which they provide
    a functional and visual analytic system for the exploration of enriched
    metabolic pathways on microbial genetic network.

Large-scale network session: Three papers are included in this session. The
    first, proposed by Tabourier et. al., tackles the problem of link prediction
    applying an original rank merging approach. The second paper, by Grube
    et. al., deals with large-scale network sampling. The last paper, proposed
    by Geigl and Helic, presents a study on alternative approaches of decen-
    tralized search, stemming from the very famous papers by Kleinberg and
    Adamic on the same topic.
Dynamic network session: This session include also three papers. The first
    one is by Redmond and Cunningham in which they propose a method to
    detect over-represented temporal motif in time-evolving network. the basic
    idea is to compare the frequency of temporal motif against that of a ran-
    dom temporal network. The second paper, by Vukadinovic Greetham and
    Ward, presents a study of dyadic and multi-actor conversations in twitter.
    Lastly, Ben Abdrabbah et al. present a framework for recommendation
    computations based on communities detected on time-stamped data.

    We would like to thank authors, Program Committee members and all addi-
tional reviewers without whom the preparation of this program would not have
been possible. Our gratitude also goes to the Computer Science Lab of the
Paris-Nord University, the University of Torino and Istituto Nazionale di Alta
Matematica that co-supported our workshop through supporting our activities.


                                                                   R. Kanawati
                                                                   R. G. Pensa
                                                                   C. Rouveirol