Boosting node activity by recommendations in social networks

Yang, Wenguo; Chen, Shengminjie; Gao, Suixiang; Yan, Ruidong

doi:10.1007/s10878-020-00629-6

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…For instance, Khalil et al [22] consider two optimization problems, i.e., adding edges to maximize influence spread and deleting edges to minimize influence spread. Yang et al [37,38] investigate how to add a limited number of edges from a candidate set to maximize the seed's influence in a directed acyclic graph under the IC model. Huang et al [18] examine the problem of selecting edges from the original network to maximize the influence of specific seeds via these edges.…”

Section: Diffusion-aware Link Manipulationmentioning

confidence: 99%

Link Recommendation to Augment Influence Diffusion with Provable Guarantees

Chen,

Song,

Tang

2024

Proceedings of the ACM Web Conference 2024

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Link recommendation systems in online social networks (OSNs), such as Facebook's "People You May Know", Twitter's "Who to Follow", and Instagram's "Suggested Accounts", facilitate the formation of new connections among users. This paper addresses the challenge of link recommendation for the purpose of social influence maximization. In particular, given a graph 𝐺 and the seed set 𝑆, our objective is to select 𝑘 edges that connect seed nodes and ordinary nodes to optimize the influence dissemination of the seed set. This problem, referred to as influence maximization with augmentation (IMA), has been proven to be NP-hard.In this paper, we propose an algorithm, namely AIS, consisting of an efficient estimator for augmented influence estimation and an accelerated sampling approach. AIS provides a (1 − 1/e − 𝜀)approximate solution with a high probability of 1 − 𝛿, and runs in 𝑂 (𝑘 2 (𝑚 +𝑛) log(𝑛/𝛿)/𝜀 2 +𝑘 |𝐸 C |) time assuming that the influence of any singleton node is smaller than that of the seed set. To the best of our knowledge, this is the first algorithm that can be implemented on large graphs containing millions of nodes while preserving strong theoretical guarantees. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposed algorithm. CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Information systems → Social networks.

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Section: Diffusion-aware Link Manipulationmentioning

confidence: 99%

Link Recommendation to Augment Influence Diffusion with Provable Guarantees

Chen,

Song,

Tang

2024

Proceedings of the ACM Web Conference 2024

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“…In particular, researchers note that social networks can filter actors, close groups, reduce the chance of unintentional communication, and isolate themselves from other groups, which causes some dosage and reduction of intergroup interaction of communities with different goals, political goals, interests, etc. [14][15][16][17]. User clustering in political discussions has been investigated in [18].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Public Health: Analysis of Opinions in Social Media

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Pilgún

2023

IJERPH

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The article presents the results of research of public opinion during the third wave of the COVID-19 pandemic in Russia. The study touches on the attitude of citizens to public health, as well as the reaction of social media users to government measures in a crisis situation during a pandemic. Special attention is paid to the phenomenon of infodemic and methods of detecting cases of the spread of false and unverified information about diseases. The article demonstrates the application of an interdisciplinary approach using network analysis of texts and sociological research. A model for detecting social stress in the textual communication of social network users using a specially trained neural network and linguistic analysis methods is presented. The validity and validity of the results of the analysis of social network data were verified using a sociological survey. This approach allows us to identify points of tension in matters of public health promotion, during crisis situations to improve interaction between the government and society, and to timely adjust government plans and actions to ensure resilience in emergency situations for public health purposes.

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