Measuring Node Similarity for the Collective Attention Flow Network
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.
- 11.Sharma, R., Montesi, D.: Investigating similarity of nodes’ attributes in topological based communities. In: The Web Conference, pp. 1253–126 (2018)Google Scholar
- 12.Hutair, M.B., Aghbari, Z.A., Kamel, I.: Social community detection based on node distance and interest. In: 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 274–289. ACM (2016)Google Scholar
- 14.Masrour, F., Barjesteh, I., Forsati, R., Esfahanian, A.H., Radha, H.: Network completion with node similarity: a matrix completion approach with provable guarantees. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 302–307. ACM (2015)Google Scholar
- 16.Conte, A., Ferraro, G., Grossi, R., Marino, A., Sadakane, K., Uno, T.: Node similarity with q-Grams for real-world labeled networks. In: 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1282–1291. ACM (2018)Google Scholar
- 18.Janssen, J., Prałat, P., Wilson, R.: Estimating node similarity from co-citation in a spatial graph model. In: ACM Symposium on Applied Computing, pp. 1329–1333. ACM (2010)Google Scholar