Research in modeling, analyzing and mining large-scale networks has attracted an increasing effort in the last few years. A major trend of work in network modeling and mining concerns analyzing homogeneous static networks (i.e. one snapshot of a network). However, in real world settings, networks are often dynamic, heterogeneous, and both nodes and links can be described by a set of attributes.
The concept of multiplex network has been recently proposed to ease modeling real work networks. A multiplex network is often represented a multi-layer network composed of a set of nodes related to each other with different types of relations. On the other hand, the concept of attributed network has also been introduced recently to take into account the fact that the vertices of the network are described by attributes like for instance their genre or age. These representations are much richer than simple complex networks often used to model complex interaction systems. However, this poses the challenge to provide adequate answers to all basic network analysis tasks that have been studied and provided in the recent few years for the case of homogeneous networks without attributes.
The goal of this workshop is make the point on new approaches for multiplex and attributed network mining. We encourage contributions on methods and techniques that are transversal to every application domains, rather than focusing on specific issues concerning each domain separately.
We will consider the two main aspects of network analysis: modeling and knowledge discovery. The technical sessions should point out this differentiation, and enforce the interaction between researcher from different research domains.

A list of non-exhaustive relevant topics include:

- Models and measures for multiplex & attributed networks.
- Co-evolution of layers in multiplex networks.
- Layer aggregation approaches.
- Vertex similarity in multiplex and attributed networks
- Community detection in multiplex network & attributed networks.
- Link prediction in multiplex and attributed networks
- Multiplex and attributed networks evolution models
- Multiplex network and dynamic network mining
- Ensemble learning for multiplex and attributed networks mining
- Multiplex and attributed networks visualisation.
- Applications of multiplex network mining and modeling.
- Mining and learning in augmented graphs
- Modeling and visualizing of augmented graphs
- Constraint-based pattern mining in augmented graphs
- Computational or statistical learning theory related to augmented graphs
- Analysis of heterogeneous network
- Analysis of multidimensional graphs
- Relationships between augmented graphs and statistical relational learning or inductive logic programming

We welcome original contributions, either theoretical or empirical, describing ongoing projects or completed work (even partially published).

The instructions for authors and the LaTeX packages can be found at http://www.springer.de/comp/lncs/authors.html.

Paper length should not exceed 12 pages.

To submit your paper(s), please log into the submission website :

https://www.easychair.org/conferences/?conf=manem15

Important dates

Paper submission April 30, 2015
Acceptance Notification May 29 , 2015
Final manuscript due
June 19, 2015