PageRank model of opinion formation on social networks

Kandiah, Vivek; Shepelyansky, Dima L.

doi:10.1016/j.physa.2012.06.047

Cited by 39 publications

(53 citation statements)

References 36 publications

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“…In the PROF model, the opinion of a node is updated by the weighted sum of neighbor nodes' opinions and the weight of the neighbor nodes are given by their PageRank (see the next section for details). It is found that a group of top influential elites in the networks (i.e., nodes with high PageRank) can impose their own opinion on a significant fraction of the considered networks [10]. The PROF model is also considered on Ulam networks [11], generated by the intermittency map and the Chirikov typical map, showing a similar behavior with the case of World Wide Web (WWW).…”

Section: Introductionmentioning

confidence: 82%

“…The PageRank opinion formation (PROF) model, introduced in [10], takes into account a node influence in the pro-cess of opinion formation. In the PROF model, the opinion of a node is updated by the weighted sum of neighbor nodes' opinions and the weight of the neighbor nodes are given by their PageRank (see the next section for details).…”

Section: Introductionmentioning

confidence: 99%

“…In the present work we consider how heterogeneous node influence affects the collective opinion formation using the modified PageRank opinion formation (PROF) model to go beyond previous works [10,11]. Our goal is to examine how the PROF model behaves on real directed networks if we adjust the heterogeneity of node influence (i.e., the PageRank of nodes).…”

Section: Introductionmentioning

confidence: 99%

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Opinion formation driven by PageRank node influence on directed networks

Eom

Shepelyansky

2015

Physica A: Statistical Mechanics and its Applications

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We study a two states opinion formation model driven by PageRank node influence and report an extensive numerical study on how PageRank affects collective opinion formations in large-scale empirical directed networks. In our model the opinion of a node can be updated by the sum of its neighbor nodes' opinions weighted by the node influence of the neighbor nodes at each step. We consider PageRank probability and its sublinear power as node influence measures and investigate evolution of opinion under various conditions. First, we observe that all networks reach steady state opinion after a certain relaxation time. This time scale is decreasing with the heterogeneity of node influence in the networks. Second, we find that our model shows consensus and non-consensus behavior in steady state depending on types of networks: Web graph, citation network of physics articles, and LiveJournal social network show non-consensus behavior while Wikipedia article network shows consensus behavior. Third, we find that a more heterogeneous influence distribution leads to a more uniform opinion state in the cases of Web graph, Wikipedia, and Livejournal. However, the opposite behavior is observed in the citation network. Finally we identify that a small number of influential nodes can impose their own opinion on significant fraction of other nodes in all considered networks. Our study shows that the effects of heterogeneity of node influence on opinion formation can be significant and suggests further investigations on the interplay between node influence and collective opinion in networks.

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Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Opinion formation driven by PageRank node influence on directed networks

Eom

Shepelyansky

2015

Physica A: Statistical Mechanics and its Applications

Self Cite

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“…The parameter Alpha reflects the relative importance of endogenous versus exogenous factors in the determination of centrality. PageRank vector is the right eigenvector with unit eigenvalue of the Google matrix constructed from the adjacency matrix of a given directed network [12]. PageRank vector is used by the Google search engine for an efficient ranking of web pages.…”

Section: Relationship Between Network Topology and Information Spreadingmentioning

confidence: 99%

Information Diffusion Model and Hadoop Implementation Research on Online Social Network

Chen¹,

Hu²

2017

dtcse

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Abstract. Social networks are currently gotten increasing focus with the growth of online social network services, such as Facebook, Sina Weibo. One major challenge for social network research is how to build information diffusion model to describe the complex information diffusion model. For this purpose, information diffusion models are researched. To show the importance of network topology, how to identify the most important spreaders in the network is a key scientific problem. Different kinds of network centrality measure methods are analyzed. The role of a potential edge in social network is analyzed. The relationship between network topology structure and information diffusion dynamics model is analyzed. Pagerank algorithm for SinaWeibo users are implemented in Hadoop cloud computing platform. Experiment result shows that Pagerank algorithm can be implemented in Hadoop cloud platform to recognize the important spreaders in social network. Big data in social network can be analyzed in the platform.

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“…In actual social networks, the interaction among agents is not always symmetrical. In [23], the authors proposed a PageRank model of opinion formation, in which connections between agents are weighted by a webpage ranking algorithm used by the Google search engine, i.e., PageRank probability. Similarly, in [24], opinion leaders with a strong convincing power were distinguished from ordinary people, and agents selected the weighted average of neighboring opinions.…”

Section: Introductionmentioning

confidence: 99%

Competition of Dynamic Self-Confidence and Inhomogeneous Individual Influence in Voter Models

Xiong

Liu

Zhu

2013

Entropy

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Abstract:In social systems, agents often have different ability to persuade neighbors to adopt their opinions. In this paper, we aim to investigate how the location and heterogeneity of influencers in social networks can improve convergence. We propose a voter model with dynamic self-conviction and heterogeneous individual influence which is related to the underlying network topology. An agent may keep its current opinion according to personal conviction, or otherwise, it may preferentially choose the opinion of the neighbor that has a great influence. Individual conviction evolves during the dynamic process, and can be strengthened by social recognition. Simulations indicate our model has three nontrivial results. First, the conservation of average magnetization in the voter model is broken under the effect of individual conviction and influence, and the system evolves to an ordered state in which one opinion is dominant, but total consensus is prevented by extremists. Furthermore, individual influence has a subtle action on opinion evolution. The heterogeneity of individual influence accelerates the relaxation process, but, with the action of dynamic conviction, more heterogeneous influence does not mean the average magnetization will be more ordered. In addition, when competing with agents' conviction, more heterogeneous individual influence plays a more significant role in agents' decisions. These results are helpful for understanding some aspects of collective phenomena that occur on online social media. OPEN ACCESSEntropy 2013, 15 5293

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PageRank model of opinion formation on social networks

Cited by 39 publications

References 36 publications

Opinion formation driven by PageRank node influence on directed networks

Opinion formation driven by PageRank node influence on directed networks

Information Diffusion Model and Hadoop Implementation Research on Online Social Network

Competition of Dynamic Self-Confidence and Inhomogeneous Individual Influence in Voter Models

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