Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy.
eCampus University, Novedrate, CO, Italy.
BMC Bioinformatics. 2022 Jan 10;22(Suppl 15):614. doi: 10.1186/s12859-022-04564-7.
Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges.
We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data.
The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.
使用网络来表示数据之间的关系在计算生物学、医学信息学和社交网络挖掘等多个研究领域中得到了广泛应用。最近，复杂网络已被引入以更好地捕捉模型场景的洞察力。其中，对偶网络 (DN) 由映射信息组成，这些信息是包含相同节点集但具有不同边的两个网络：一个称为物理网络，具有无权重边，另一个称为概念网络，具有权重边。
我们专注于 DNs，并提出了一种在具有特定属性的 DNs 中查找公共子图（又名社区）的工具。该工具称为 Dual-Network-Analyser，它基于识别在概念网络中诱导最优模块化子图且在物理网络中为连通子图的社区。它包括应用于所考虑情况的 Louvain 算法。Dual-Network-Analyser 可用于研究 DNs 以查找公共模块化社区。我们报告了使用该工具在社交网络和生物数据中的合成 DNs 以及真实案例中识别社区的结果。
该方法已通过使用合成和生物网络进行了测试。结果表明，它能够很好地从 DNs 中检测到有意义的信息。
