Complex network analysis techniques provide powerful approaches for gaining deep insights into the behaviours of complex systems composed of a high dimensional number of interacting objects. They are already applied to a wide variety of applications including both online and offline social networks, system biology, information retrieval, natural language processing, peer-to-peer networks, direct marketing, recommender systems and much more.
There are clear indications that large real-world networks evolve following self-organising principles and evolutionary laws that cross disciplinary boundaries. Real-world complex networks actually share a set of topological features that distinguish them from pure random networks. Exhibition of a mesoscopic structure, often denoted by community level, is one of the main characteristics of real-world complex networks.
Identifying relevant communities is a central question in various applications. A community can be a set of genes or proteins involved in a biological function, a set of documents or web pages related to a given topic, or a set of similar customers or users having the same profile in a social network. Devising efficient algorithms for community detection in very large-scale networks has gained much attention in the last few years.
A main trend in this area focuses on detecting disjoint communities in static networks applying an optimisation scheme of a quality function of a graph partition (namely the modularity criteria introduced by Newman). Recent evolutions argue for renewing efforts in this field. For instance:
- The quick pace of growth of handled networks does not allow having a global knowledge of network topology. This makes it hard to evaluate the quality of a partition.
- Recent studies have shown that modularity suffers from some serious drawbacks including a resolution limit and a discernment problem. The first problem refers to the incapacity of modularity optimisation-driven approaches in detecting communities whose size is below a given threshold. The second problem refers to the fact that many different partitions of a graph may have a high score in terms of their modularity.
- Almost all studied complex networks are highly dynamic in that both nodes and edges can vary with time. Furthermore, in many applications one node may belongs to more than one community at the same time.
Another highly promising research topic concerning large real networks concerns modelling their dynamics. Indeed, most often, data about these networks has been collected at different time points. This dynamic view of the system allows the time component to play a key role in the comprehension of the evolution of the network structure and/or of flows within those networks. Time can help to determine the real causal relationships within a network, for a better understanding, for instance, of gene activations within a regulation network, or link creation/deletion within a collaboration network, or opinion or disease diffusion within a social network.
Handling such dynamic data is a also a major challenge for current pluri-disciplinary research, in particular in machine learning and data mining, and has led to the development of recent innovative techniques that consider complex/multi-level time-evolving networks, graphs and potentially heterogeneous nodes and links.
This special issue aims at attracting contributions addressing all aspects of dynamic networks analysis: large real network analysis and modelling, and knowledge discovery within those dynamic networks.
A number of works have been recently proposed to handle one or more of these issues. The purpose of this issue is to provide a review of recent innovative approaches for node clustering and evolution mining in dynamic large-scale complex networks.
This special issue follows MARAMI’12: the Third French Conference on Modelling and Analysing Networks: Mathematical and Algorithmic Approaches at Villetaneuse, France in October 2012. While authors of papers accepted to MARAMI are invited to submit revised and substantially extended versions of their work, we also strongly encourage researchers unable to participate in the conference to submit articles for this call.
Suitable topics include but are not limited to:
- Dynamic networks modelling:
- Generative models
- Network inference from raw data
- Visualisation approaches for large-scale dynamic networks
- Storage and summarisation of dynamic networks
- Community detection approaches:
- Disjoint/overlapping community detection algorithms
- Community detection in heterogeneous networks
- Community detection in dynamic networks
- Local approaches for community detection/ego-centric communities
- Evaluation metrics
- Interpretation approaches of detected communities
- Network evolution mining:
- Link prediction
- Network evolution rules learning
- Frequent graph mining patterns
- Spreading models
- Epidemical and influence networks
- Social networks: online social networks, folksonomies,
- Bibliographical networks: co-authorship, citation, bibliographical coupling, digital libraries, collaboration networks
- Sensor networks, peer-to-peer networks, internet, web agent networks, body sensor networks
Full paper submission: 29 April, 2013 (extended)
Notification date: 3 June, 2013
Final paper submission: 30 June, 2013
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