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but would someone please tell me what an Attributed Network is
   Barry Wellman

   Step by step, link by link, putting it together--Streisand/Sondheim
        The earth to be spannd, connected by network--Walt Whitman
              It's Always Something--Roseanne Roseannadanna
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
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---------- Forwarded message ----------
Date: Thu, 19 Apr 2018 21:02:37 +0200
From: VAN METER Karl <[log in to unmask]>
Reply-To: [log in to unmask], VAN METER Karl <[log in to unmask]>
To: [log in to unmask], [log in to unmask]
Cc: [log in to unmask]
Subject: [bms-rc33] Meeting - Mining Attributed Networks Workshop @ WebConf2018
     (23 Apr, Lyon FR)

Thanks to Rushed Kanawati


MATNet'18: International workshop on Mining Attributed Networks @
WWW'2018 (The Web Conf 2018)
23 April 2018, Lyon France
https://urldefense.proofpoint.com/v2/url?u=http-3A__www-2Dlipn.univ-2Dparis13.fr_man-2Dwww2018&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=K2ijanc2qg_hSEBpu5_1fZiu5tKq4L7YJ3dRAdcxZs4&s=CsaJw4hfTkOE2uFcKx1ca0UMvfKhyWQFRHyG64Xbx8c&e=

CONTEXT

Attributed network models have seen an increasing success in recent
years, thanks to their informative power and to their ability to model
complex networked relations that characterize most real-world phenomena.
Th eir use has been attractive to communities in different disciplines
such as computer science, physics, social science, as well as in
interdisciplinary research environments. The use of such models has been
also supported by the increasing easiness in collecting multi-relational
data from the Web, e.g., from online social media platforms,
crowdsourced data, online knowledge base. Within this view, the World
Wide Web is an inestimable source of information, which can be
conveniently represented with feature-rich network models, e.g.,
enclosing temporal aspects of the data, quantitative and/or qualitative
properties of nodes, different relations between a common set of
entities, different existence probabilities, or modeling connection
between different entity types.

The aim of the MATNet workshop, that will be held in conjunction with
The Web Conf 2018 in Lyon, is to get an insight in the current status of
research in network analysis and mining, showi ng how modeling
information coming from the World Wide Web in Attributed Network models
can make it possible to focus on domains and research questions that
have not been deeply investigated so far and to improve solutions to
classic tasks. We will solicit contributions that aim to focus on the
analysis of attributed networks, addressing important principles,
methods, tools and future research directions in this emerging field,
possibly being  transversal to different application domains. In
particular, we cover the modeling of complex networks, multiplex
networks, and any unsupervised, supervised, and semi-supervised mining
approach in attributed network contexts.

Topics

- A list of non-exhaustive relevant topics include:
- Models and measures for multiplex  & attributed networks.
- Foundations of Learning and Mining in Attributed Networks
- Centrality and Ranking in Attributed Networks
- Community Detection in Attributed Networks
- Link Prediction in Attributed Networks
- Simplification/pruning/sampling of Attributed Networks
- User Behavior Modeling in Attributed Networks
- Social Influence and Information difusion in Attributed Networks
- Reputation and Trust in Attributed Networks
- Embedding and Deep Learning in Attributed Networks
- Probabilistic and Uncertain Attributed Networks
- Time-evolving Attributed Networks
- Hypergraph-based modeling, analysis and learning problems
- Cross-Domain problems in Attributed Networks
- Visualization of Attributed Networks
- Personalization and Recommendation in Attributed Networks
- Mobility in Attributed Networks
- Vertex similarity in multiplex and attributed networks
- Multiplex and attributed networ ks evolution models
- Multiplex network and dynamic network mining
- Ensemble learning for multiplex and attributed network mining
- Pattern mining in attributed networks

>> Consider submitting your methodologically interesting articles to the BMS <<
       >> Soumettez vos articles méthodologiquement intéressants au BMS <<
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