Ma Xiaoke, Zhao Wei, Wu Wenming
IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar-Apr;20(2):1170-1179. doi: 10.1109/TCBB.2022.3176859. Epub 2023 Apr 3.
Multi-layer networks provide an effective and efficient tool to model and characterize complex systems with multiple types of interactions, which differ greatly from the traditional single-layer networks. Graph clustering in multi-layer networks is highly non-trivial since it is difficult to balance the connectivity of clusters and the connection of various layers. The current algorithms for the layer-specific clusters are criticized for the low accuracy and sensitivity to the perturbation of networks. To overcome these issues, a novel algorithm for the layer-specific module in multi-layer networks based on nonnegative matrix factorization (LSNMF) is proposed by explicitly exploring the specific features of vertices. LSNMF first extract features of vertices in multi-layer networks by using nonnegative matrix factorization (NMF) and then decompose features of vertices into the common and specific components. The orthogonality constraint is imposed on the specific components to ensure the specificity of features of vertices, which provides a better strategy to characterize and model the structure of layer-specific modules. The extensive experiments demonstrate that the proposed algorithm dramatically outperforms state-of-the-art baselines in terms of various measurements. Furthermore, LSNMF efficiently extracts stage-specific modules, which are more likely to enrich the known functions, and also associate with the survival time of patients.
多层网络提供了一种有效且高效的工具，用于对具有多种类型相互作用的复杂系统进行建模和表征，这与传统的单层网络有很大不同。多层网络中的图聚类非常复杂，因为很难平衡簇的连通性和各层之间的连接。当前用于特定层簇的算法因准确性低和对网络扰动敏感而受到批评。为了克服这些问题，通过明确探索顶点的特定特征，提出了一种基于非负矩阵分解的多层网络特定层模块新算法（LSNMF）。LSNMF首先使用非负矩阵分解（NMF）提取多层网络中顶点的特征，然后将顶点特征分解为公共和特定分量。对特定分量施加正交约束以确保顶点特征的特异性，这为表征和建模特定层模块的结构提供了更好的策略。大量实验表明，所提出的算法在各种度量方面显著优于现有最先进的基线算法。此外，LSNMF有效地提取了阶段特定模块，这些模块更有可能丰富已知功能，并且还与患者的生存时间相关。
