Can A User Guess What Her Followers Want?

De, Abir; Singla, Adish; Upadhyay, Utkarsh; Gomez-Rodriguez, Manuel

doi:10.1145/3336191.3371803

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…It is based on the premise that superiority and closeness attract followers. Closeness reflects the closeness of followers to their leaders, whereas superiority measures supremacy (Ni et al, 2021;De et al, 2020). However, PageRank and neighborhood of a node in graph-based data have been scarcely reported in the literature.…”

Section: Graph Theoretic Approach Based On Similaritiesmentioning

confidence: 99%

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A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

Bhattacharya

Nagwani

Tripathi

2022

DTA

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PurposeSocial networking platforms are increasingly using the Follower Link Prediction tool in an effort to expand the number of their users. It facilitates the discovery of previously unidentified individuals and can be employed to determine the relationships among the nodes in a social network. On the other hand, social site firms use follower–followee link prediction (FFLP) to increase their user base. FFLP can help identify unfamiliar people and determine node-to-node links in a social network. Choosing the appropriate person to follow becomes crucial as the number of users increases. A hybrid model employing the Ensemble Learning algorithm for FFLP (HMELA) is proposed to advise the formation of new follower links in large networks.Design/methodology/approachHMELA includes fundamental classification techniques for treating link prediction as a binary classification problem. The data sets are represented using a variety of machine-learning-friendly hybrid graph features. The HMELA is evaluated using six real-world social network data sets.FindingsThe first set of experiments used exploratory data analysis on a di-graph to produce a balanced matrix. The second set of experiments compared the benchmark and hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments show that the proposed (HMELA) method predicts missing links better than other methods.Practical implicationsA hybrid suggested model for link prediction is proposed in this paper. The suggested HMELA model makes use of AUC scores to predict new future links. The proposed approach facilitates comprehension and insight into the domain of link prediction. This work is almost entirely aimed at academics, practitioners, and those involved in the field of social networks, etc. Also, the model is quite effective in the field of product recommendation and in recommending a new friend and user on social networks.Originality/valueThe outcome on six benchmark data sets revealed that when the HMELA strategy had been applied to all of the selected data sets, the area under the curve (AUC) scores were greater than when individual techniques were applied to the same data sets. Using the HMELA technique, the maximum AUC score in the Facebook data set has been increased by 10.3 per cent from 0.8449 to 0.9479. There has also been an 8.53 per cent increase in the accuracy of the Net Science, Karate Club and USAir databases. As a result, the HMELA strategy outperforms every other strategy tested in the study.

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Section: Graph Theoretic Approach Based On Similaritiesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

Bhattacharya

Nagwani

Tripathi

2022

DTA

View full text Add to dashboard Cite

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“…Modern Internet users want to evaluate publications, goods and services, preferably using simple and familiar tools. Studies presented in [20] show that a significant proportion of social media users take into account the evaluation of previous publications when publishing another post. The use of simple assessment methods can serve as an incentive for developers and sellers of goods and services, as well as for content authors of corporate websites to take into account the preferences of visitors and buyers.…”

Section: Review Of Ways To Implement Feedbackmentioning

confidence: 99%

Implementation of Feedback as an Important Element of Modern Corporate Websites

Golchevskiy

Yermolenko

2021

JIOS

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The Internet has become an independent global electronic environment for economic activity, which for many companies has come to the forefront, not only complementing but also replacing the business methods used for decades. Almost any company, linking its activities to the Internet, develops and maintains its representation in the form of a corporate portal or website. Sites become more functional, more beautiful and informative over time, but many managers forget or do not pay due attention to such an important element of the site as feedback from external and internal contractors. This article presents the effectiveness of using feedback on specific examples in a simple and straightforward way, and gives an overview of the different forms of organisation of such communication, with an indication of possible tools for their implementation.

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On Followers Search

Luo

Zhu

et al. 2021

IEEE Trans. Comput. Soc. Syst.

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Can A User Guess What Her Followers Want?

Cited by 3 publications

References 28 publications

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

Implementation of Feedback as an Important Element of Modern Corporate Websites

On Followers Search

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