Measuring Node Similarity for the Collective Attention Flow Network
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Quantifying the similarity of nodes in collective attention flow network has an important theoretical and practical value. In this paper, we defined the generation time Rt, the influence radius Sr and the representation Vs (Rt, Sr) of the nodes in the collective attention flow network based on the optimization of Spatial Preferred Attachment (SPA) model. NID algorithm, based on the influence distance Sd that was calculated by the spatial norm, to measure the similarity of the nodes in the collective attention flow network was proposed. Experiments show that our algorithm not only accurately quantify the similarity of nodes in the collective attention flow network, but has a higher universality.
KeywordsSpatial Preferred Attachment (SPA) Collective attention flow network Node similarity algorithm
This paper was supported by the Natural Science Foundation of China (No. 71764025, 61863032, 61662070); the Research Project on Educational Science Planning of Gansu, China (Grant No. GSGHBBKZ021, GSGHBBKW007); the Scientific Research Foundation of the Higher Education Department of Gansu, China (Grant No. 2018A-001). Author contributions: Manfu Ma and Zhangyun Gong are co-first authors who jointly designed the research. Correspondence and requests for materials should be addressed to Yong Li.
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