Modeling and analyzing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval, etc. Nowadays, scientific communities have access to very large volumes of network-structured data, such as social networks, gene/proteins/metabolic networks, sensor networks, and peer-to-peer networks. Often, data is collected at different time points allowing to capture a dynamic trend of the observed network. Consequently, the time component plays a key role in the comprehension of the evolutionary behavior of the studied 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, and it has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs, heterogeneous information (nodes and links), requiring scalable algorithms that are able to manage large-scale complex networks. This special issue is the follow-up of the second Dynamic Networks and Knowledge Discovery workshop (DyNaK)1 that was held in conjunction to ECML-PKDD 2014 at Nancy on September 15th 2014. The workshop was motivated by the interest of providing a meeting point for scientists with different backgrounds that were interested in the study of large complex networks and the dynamic aspects of such networks. The workshop attracted 15 submissions out of which 8 papers were accepted. Building on the success of the DyNaK workshop, an open call for papers was issued for this special issue, focusing on the major topic discussed in the workshop: analyzing, modeling and mining large-scale real networks. More than 50 abstracts were received in a first preliminary step, among which 25 were selected for proceeding to long paper submission. 17 high quality papers were received at this second step, each of which was reviewed by three reviewers. Only three contributions were finally selected and are part of this special issue.
Special Issue on Dynamic Networks and Knowledge Discovery
Ruggero G. Pensa;
2017-01-01
Abstract
Modeling and analyzing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval, etc. Nowadays, scientific communities have access to very large volumes of network-structured data, such as social networks, gene/proteins/metabolic networks, sensor networks, and peer-to-peer networks. Often, data is collected at different time points allowing to capture a dynamic trend of the observed network. Consequently, the time component plays a key role in the comprehension of the evolutionary behavior of the studied 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, and it has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs, heterogeneous information (nodes and links), requiring scalable algorithms that are able to manage large-scale complex networks. This special issue is the follow-up of the second Dynamic Networks and Knowledge Discovery workshop (DyNaK)1 that was held in conjunction to ECML-PKDD 2014 at Nancy on September 15th 2014. The workshop was motivated by the interest of providing a meeting point for scientists with different backgrounds that were interested in the study of large complex networks and the dynamic aspects of such networks. The workshop attracted 15 submissions out of which 8 papers were accepted. Building on the success of the DyNaK workshop, an open call for papers was issued for this special issue, focusing on the major topic discussed in the workshop: analyzing, modeling and mining large-scale real networks. More than 50 abstracts were received in a first preliminary step, among which 25 were selected for proceeding to long paper submission. 17 high quality papers were received at this second step, each of which was reviewed by three reviewers. Only three contributions were finally selected and are part of this special issue.
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