Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

Liu, Xiaoyang; He, Daobing

doi:10.1093/comjnl/bxz104

Cited by 14 publications

(13 citation statements)

References 15 publications

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“…As an interdisciplinary subject, there are examples of applying various computer technologies to the research of complex networks. For example, Liu et al proposed an artificial neural network-based model for information dissemination and opinion evolution, IPNN (Information propagation and public opinion evolution model based on artificial neural network, IPNN) [12]. Fan et al proposed a reinforcement learning-based algorithm for node importance identification in complex networks, FINDER.…”

Section: Introductionmentioning

confidence: 99%

Mining Algorithm of Relatively Important Nodes Based on Edge Importance Greedy Strategy

Li¹,

Yin²,

Wang

et al. 2022

Applied Sciences

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Relatively important node mining has always been an essential research topic in complex networks. Existing relatively important node mining algorithms suffer from high time complexity and poor accuracy. Therefore, this paper proposes an algorithm for mining relatively important nodes based on the edge importance greedy strategy (EG). This method considers the importance of the edge to represent the degree of association between two connected nodes. Therefore, the greater the value of the connection between a node and a known important node, the more likely it is to be an important node. If the importance of the edges in an undirected network is measured, a greedy strategy can find important nodes. Compared with other relatively important node mining methods on real network data sets, such as SARS and 9/11, the experimental results show that the EG algorithm excels in both accuracy and applicability, which makes it a competitive algorithm in the mining of important nodes in a network.

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Section: Introductionmentioning

confidence: 99%

Mining Algorithm of Relatively Important Nodes Based on Edge Importance Greedy Strategy

Li¹,

Yin²,

Wang

et al. 2022

Applied Sciences

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“…In this environment, live streaming information could be transmitted from merchants to users and disseminated actively among social users. 4,5 Therefore, research on the dissemination mechanism of live streaming information in multiple complex social networks and its effect on user behavior is a valuable insight for academics.…”

Section: Introductionmentioning

confidence: 99%

A game model for information dissemination in live streaming e‐commerce environment

Liu

Yao

Wang

et al. 2021

Int J Communication

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As a new form of social e-commerce, live streaming e-commerce is becoming increasingly popular among Chinese consumers. Live streaming brings huge opportunities, and we can take measures to further expand the dissemination of live streaming information. A live streaming information dissemination game model based on social evolutionary game theory is presented to simulate multiple complex live streaming e-commerce networks. The introduction of incentive mechanism in the model further quantifies the internal relation between rewards and live streaming information dissemination to a certain extent and predicts the ratings of live streaming in networks with different update frequency of social relationships. The simulation results indicate that the reputation environment in social networks, adjusting frequency of relationship, pre-broadcast rewards to post-broadcast rewards ratio all have certain ranges of impact on information dissemination in live streaming e-commerce environment and then affect the live streaming ratings. These findings offer insights into the dissemination of live streaming information in social networks.

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“…e information diffusion on the online media is faster, deeper, wider, and harder to control compared with traditional media such as newspapers and broadcasts [1]. Hence, for public opinion government [2,3] and marketing [4][5][6][7][8], it is important to mine the law behind information diffusion on online media [5]. In the past decade, researchers begin to predict the future trends in information diffusion [9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction

Huang¹,

Zhou²,

Zhang³

et al. 2021

Computational Intelligence and Neuroscience

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Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in information cascade prediction, we propose a Diffusion Source latent Semantics-Fused cascade prediction framework, named DSSF. Specifically, we firstly apply diffusion sources embedding to model the special role of the source users. To learn the latent interaction between users and diffusion sources, we proposed a co-attention-based fusion gate which fuses the diffusion sources’ latent semantics with user embedding. To address the challenge that the distribution of diffusion sources is long-tailed, we develop an adversarial training framework to transfer the semantics knowledge from head to tail sources. Finally, we conduct experiments on real-world datasets, and the results show that modelling the diffusion sources can significantly improve the prediction performance. Besides, this improvement is limited for the cascades from tail sources, and the adversarial framework can help.

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Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

Cited by 14 publications

References 15 publications

Mining Algorithm of Relatively Important Nodes Based on Edge Importance Greedy Strategy

Mining Algorithm of Relatively Important Nodes Based on Edge Importance Greedy Strategy

A game model for information dissemination in live streaming e‐commerce environment

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction

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