Workshop on dynamic networks and knowledge discovery, Barcelona, Spain
24 September 2010 - 24 September 2010
Spain
A workshop on dynamic networks and knowledge discovery will be held in Barcelona, Spain on 24 September 2010.
The event will be a meeting point for scientists interested in the study of large complex networks and the dynamic aspects of such networks. It aims to cover both aspects of networks analysis: large real network analysis and modelling, and knowledge discovery within those networks.
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. Often, these data are not only static, but collected at different time points. This dynamic view allows the time component to play a key role in the comprehension of the evolutionary behaviour of the network (evolution of the network structure and/or of flows within the system). Time can help to determine the real causal relationships within, for instance, gene activations, link creation and information flow.
Handling such data is a major challenge for current research in machine learning and data mining. This has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs and heterogeneous information (nodes and links). It also requires scalable algorithms that are able to manage huge and complex networks.
Modeling and analysing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval, etc. By connecting objects, it is possible to obtain an intuitive and global view of the relationships between components of a complex system.For further information, please visit:
http://kdd.di.unito.it/DyNaK2010/(opens in new window)
The event will be a meeting point for scientists interested in the study of large complex networks and the dynamic aspects of such networks. It aims to cover both aspects of networks analysis: large real network analysis and modelling, and knowledge discovery within those networks.
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. Often, these data are not only static, but collected at different time points. This dynamic view allows the time component to play a key role in the comprehension of the evolutionary behaviour of the network (evolution of the network structure and/or of flows within the system). Time can help to determine the real causal relationships within, for instance, gene activations, link creation and information flow.
Handling such data is a major challenge for current research in machine learning and data mining. This has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs and heterogeneous information (nodes and links). It also requires scalable algorithms that are able to manage huge and complex networks.
Modeling and analysing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval, etc. By connecting objects, it is possible to obtain an intuitive and global view of the relationships between components of a complex system.For further information, please visit:
http://kdd.di.unito.it/DyNaK2010/(opens in new window)