Heuristics and metaheuristics for biological network alignment: A review

Ma, Lijia; Shao, Zengyang; Huang, Jiaxiang; Wang, Shiqiang; Lin, Qiuzhen; Li, Jianqiang; Gong, Maoguo; Nandi, Asoke K.

doi:10.1016/j.neucom.2021.08.156

Cited by 11 publications

(1 citation statement)

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“…Apart from social networks, the typed edge graphlets approach could be convenient in studying biological networks, especially molecular graphs, where link attributes or bond types are essential information. The proposed approach is promising to be applied in biological network alignment, which aims to find a node mapping between molecular networks that reveals similar network regions [20,51]. Moreover, inspired by recent works that include subgraph counting in Graph Neural Networks [52,53], an interesting avenue is to incorporate the edge type enhanced structural information in GNN's message passing scheme.…”

Section: Plos Onementioning

confidence: 99%

Encoding edge type information in graphlets

et al. 2022

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Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements.

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Section: Plos Onementioning

confidence: 99%