Multi-hop assortativities for networks classification
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Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when 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 for scientific collaboration networks. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of node 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
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