2018/09/30 by Leonardo Gutierrez Gomez, Leonardo Gutiérrez-Gómez, Jean-Charles Delvenne
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Advanced Graph Neural Networks #Assortativity #Bioinformatics and Genomic Networks #Cheminformatics #Complex network #Computational Drug Discovery Methods #Field (mathematics) #Node (physics) #Path (computing) #Similarity (geometry) #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.1093/comnet/cny034
20 pages, 7 figures. arXiv admin note: text overlap with arXiv:1705.10817
openalex created_date 2018/09/27 · arxiv created 2018/11/16 · openalex publication_date 2018/11/23 · arxiv updated 2018/11/26 · openalex updated_date 2026/08/05
Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.