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 network.

Handling such data is a major challenge 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. The special issue, 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.

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