A novel relationship strength model for online social networks

Ju, Chunhua; Tao, Wanqiong

doi:10.1007/s11042-017-4408-4

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…However, a single dimensional method might not accurate. Besides, prior studies [ 3 , 61 , 62 , 63 , 64 ] have noted the importance of multidimensional factors (attributes) analysis in influencer detection of online social networks, however, very little was found in the literature on the modeling of multidimensional influence. This study set out with the aim of assessing the influence of users in online social networks by multidimensional factors analysis and multidimensional influence modeling.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration

Zhuang

2021

Heliyon

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Online Social networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify influencers is thus very significant, allowing us to control the outbreak of public negative opinion, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The identification of influencers attracts increasing attentions from both computer science and communication science, with multiple dimensional metrics ranging from structure-based to information-based and action-based. However, most work simply rely on one dimensional metrics. Therefore, in this paper, we analyze three dimensional characteristics (structure-based, information-based, and action-based factors) to develop the multidimensional social influence (MSI) measurement approach. With topic distillation and conditional expectation, the MSI approach can not only measure users topic-level influence, but also measure users global-level influence. Based on data collected from SinaWeibo.com, the experimental results show that the proposed framework outperforms two traditional methods (LeaderRank and FBI) both on the topic-level and the global-level. The proposed framework can be effectively applied to promote word-of-mouth marketing, and to steer public opinion in certain directions, even to support decisions during a negotiation process.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration

Zhuang

2021

Heliyon

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“…where C i is the friend set of target user u i , and C j is the friend set of source user u j . What is more, in fact, some earlier studies [18,34] demonstrated that direction is one of characterization of relations. Garton, Haythornthwaite and Wellman [35] proposed the ties changed in content, direction and strength.…”

Section: Calculation Of Common Friend Rate and The Similarity Of Usermentioning

confidence: 92%

“…Luarn and Chiu [17] predicted the relationship strength by the similarity of the profile information and interaction data between users, thus distinguished the strong and weak relationships on social network sites. Ju and Tao [18] estimated the user's similarity based on the profile information and the following information of the official accounts that is concerned by users. Then, the similarity, timeliness and interaction were confused to improve the accuracy of the relationship strength calculation to some extent.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The more common friends of the user pairs and the more similar their social circle is, the tighter the relational network will be [18]. Therefore, it is necessary to quantify design measurements for the factor of a common friend.…”

Section: Calculation Of Common Friend Rate and The Similarity Of Usermentioning

confidence: 99%

“…Based on the definition of relationship strength given by Granovetter [11], many existing research utilizes the linear combination model to calculate the relationship strength with some progress [18,42,43]. In this paper, a linear combination model is adopted, then interaction strength, common friend rate and the similarity of feature attribute are integrated as three dimensions to estimate the relationship strength, and the activity field preference and interactive habit of an individual are introduced.…”

Section: The Estimation Of Relationship Strengthmentioning

confidence: 99%

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Research on Relationship Strength under Personalized Recommendation Service

Tao

2020

Sustainability

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Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.

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