EndNote

Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural features. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. Within each such class, networks describing similar systems tend to have similar features. This occurs presumably because networks representing similar systems would be expected to be generated by a shared set of domain-specific mechanisms, and it should therefore be possible to classify networks based on their features at various structural levels. Here we describe and demonstrate a new hybrid approach that combines manual selection of network features of potential interest with existing automated classification methods. In particular, selecting well-known network features that have been studied extensively in social network analysis and network science literature, and then classifying networks on the basis of these features using methods such as random forest, which is known to handle the type of feature collinearity that arises in this setting, we find that our approach is able to achieve both higher accuracy and greater interpretability in shorter computation time than other methods

Biological network · Class (philosophy · Data mining · Feature (linguistics · Feature selection · Interpretability · Machine learning · Set (abstract data type · Advanced Graph Neural Networks · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Mathematics · Artificial Intelligence · Theoretical Computer Science

• Network classification-based structural analysis of real networks and their model-generated counterparts

  Open Access•Marcell Nagy, Roland Molontay•Network Science•2022

  Add to list