Author(s)

Wang Peng ¹

Affiliation(s)

¹ School of Economics and Management, Dalian University, No.10, Xuefu Avenue, Economic & Technical Development Zone, Dalian, Liaoning，The People's Republic of China（PRC）

Corresponding Author

Wang Peng

ABSTRACT

Along with the creation and exchange of a large amount of user content, large-scale interactive data and complex user relationships have emerged in the social network platform, which has attracted more and more researchers' attention. However, the existing research on relationship strength is mostly from user feature attributes. Similarity and social mobility are carried out, ignoring the influence of network structure on relationship strength, and does not consider the directionality and habitual problems of social power. Based on this, this paper proposes an asymmetric social network user relationship strength calculation. Method, the error method combines the user feature attribute similarity, the network structure connection strength, the social interaction strength three dimensions to comprehensively calculate the user relationship. When calculating the network topology connection strength, not only considers the number of neighbor nodes between users, but also considers The directionality and habituation of the social interaction force of the neighbor nodes will affect the user's perception of the relationship strength. Therefore, this paper calculates the contribution weight of different social forces, and finds the user's social strength from the interaction. The proposed method of asymmetric user relationship strength can Research findings and information dissemination mechanism microblogging opinion leaders in customer relationship strength forecast accuracy helps.

KEYWORDS

Asymmetry, network structure, social habits, user relationship strength

CITE THIS PAPER

Wang Peng, Asymmetric user relationship strength calculation integrated into network structure and social habits. Automation and Machine Learning(2019) Vol. 1: 1-4. DOI: http://dx.doi.org/10.23977/jeleic.2019.11001.

REFERENCES

[1] Pouria Sadeghi-Tehran, (2017) Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping, Plant methods, 1，96-108
[2] Sung Kyun Park. (2015) Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES, Environmental Health, 1, 356-374.
[3] Tanchanok Wisitponchai. (2018) AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking. BMC Bioinformatics, 2, 119-136.

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