ArticleApplied network science2021
A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks.
Article in Applied network science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future.Vaccines · 2023Article
- Temporal networks in biology and medicine: a survey on models, algorithms, and tools.Network modeling and analysis in health informatics and bioinformatics · 2023Review
- A memetic algorithm for finding multiple subgraphs that optimally cover an input network.PloS one · 2023Article
- Using dual-network-analyser for communities detecting in dual networks.BMC bioinformatics · 2022Article
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3 authors.
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Abstract
The use of networks for modelling and analysing relations among data is currently growing. Recently, the use of a single networks for capturing all the aspects of some complex scenarios has shown some limitations. Consequently, it has been proposed to use Dual Networks (DN), a pair of related networks, to analyse complex systems. The two graphs in a DN have the same set of vertices and different edge sets. Common subgraphs among these networks may convey some insights about the modelled scenarios. For instance, the detection of the Top-k Densest Connected subgraphs, i.e. a set k subgraphs having the largest density in the conceptual network which are also connected in the physical network, may reveal set of highly related nodes. After proposing a formalisation of the approach, we propose a heuristic to find a solution, since the problem is computationally hard. A set of experiments on synthetic and real networks is also presented to support our approach.
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