Contagion of Information on Two-Layered Weighted Complex Network

Zhu, Xuzhen; Yang, Qiwen; Tian, Hui; Ma, Jun; Wang, Wei

doi:10.1109/access.2019.2948941

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…People are more likely to obtain information from their friends and family than from complete strangers. The influence of intimacy heterogeneity on information dissemination was confirmed by the researchers, who built the connective links between people as edges with diverse weight distribution [14,15].…”

Section: Introductionmentioning

confidence: 88%

Social contagion influenced by active-passive psychology of college students

Ye¹,

Chen²

2022

Front. Phys.

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Educational behavioral psychology refers to the fact that college students within campus networks have various psychological cognition toward novel information and behavior. This is hardly ever taken into account or theoretically examined in weighted network research. According to psychological traits and a student’s willingness to adopt fresh behaviors, we categorize students’ behaviors into the active and passive. On this basis, a threshold models is established for the behavior of active and passive students in weighted networks, and the influence behavioral psychology on information propagation is discussed. In order to qualitatively investigate the information propagation mechanism, a partition theory based on edge-weight and behavioral psychology is developed. Active students encourage the acceptance of new behaviors and the spread of information, according to theoretical study and simulation results. However, the phase transition intersected was more significant. When the percentage of enrolled pupils is high, a continuous phase transition is present in the growth pattern of the final adoption size. In contrast, as the proportion of active students declines, the increasing pattern alterss to discontinuous phase transition. In addition, weight distribution heterogeneity facilitates the dissemination of information and does not alter phase transition pattern. Finally, the theoretical analysis is in good agreement with the simulation results.

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Section: Introductionmentioning

confidence: 88%

Social contagion influenced by active-passive psychology of college students

Ye¹,

Chen²

2022

Front. Phys.

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“…Individuals are more susceptible to receive information from those close to them, such as friends and families, rather than strangers. e researchers constructed the connective relationships between individuals as edges with heterogeneous weight distribution and verified the influence of intimacy heterogeneity on information propagation [29,30].…”

Section: Introductionmentioning

confidence: 99%

Information Propagation Influenced by Population Heterogeneity Behavioral Adoption on Weighted Network

Cui

Tian

et al. 2022

Complexity

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In the realistic world, various individuals have distinct personalities, preferences, and attitudes toward new information and behavior acceptance, called population heterogeneity. It is seldom taken into account and theoretically analyzed in information propagation on a weighted network. Therefore, we divide individuals into fashionable and conservative individuals according to their passion degree and willingness for novel behaviors acceptance. Then, we build two behavior adoption threshold models corresponding to fashionable and conservative individuals on the weighted network to explore the effect of population heterogeneity on information propagation. Next, a partition theory based on edge weight and population heterogeneity is proposed to qualitatively analyze the information propagation mechanism. The theoretical analyses and simulation results show that fashionable individuals promote information propagation and behavior adoption. More importantly, the crossover phenomena of phase transition appear. When the fraction of fashionable individuals is relatively large, the increasing pattern of the final adoption size shows a second-order continuous phase transition. In comparison, the increasing pattern alters to first-order discontinuous phase transition with the decrease of the fraction of fashionable individuals. Moreover, reducing weight distribution heterogeneity promotes information propagation and slightly accelerates the change of the phase transition pattern from the first-order discontinuous to the second-order continuous. Besides, increasing the degree distribution heterogeneity accelerates the change of the phase transition pattern. Finally, our theoretical analyses coincide well with the simulation results.

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“…ere is no complete end-to-end path between nodes in opportunistic social networks [4]; it uses the encounter opportunities brought by node movement to communicate hop by hop [5]. At present, opportunistic social network has widespread use in various fields, such as mobile phones [6], handheld electronic devices [7], vehicular networks with mobile intelligent devices on the road [8], wildlife tracking [9], and network transmission in remote areas [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Transmission for Community Detection Algorithm Based on Node Similarity in Opportunistic Social Networks

Xiaokaiti

Qian

2021

Complexity

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With the rapid development of 5G era, the number of messages on the network has increased sharply. The traditional opportunistic networks algorithm has some shortcomings in processing data. Most traditional algorithms divide the nodes into communities and then perform data transmission according to the divided communities. However, these algorithms do not consider enough nodes’ characteristics in the communities’ division, and two positively related nodes may divide into different communities. Therefore, how to accurately divide the community is still a challenging issue. We propose an efficient data transmission strategy for community detection (EDCD) algorithm. When dividing communities, we use mobile edge computing to combine network topology attributes with social attributes. When forwarding the message, we select optimal relay node as transmission according to the coefficients of channels. In the simulation experiment, we analyze the efficiency of the algorithm in four different real datasets. The results show that the algorithm has good performance in terms of delivery ratio and routing overhead.

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Contagion of Information on Two-Layered Weighted Complex Network

Cited by 15 publications

References 30 publications

Social contagion influenced by active-passive psychology of college students

Social contagion influenced by active-passive psychology of college students

Information Propagation Influenced by Population Heterogeneity Behavioral Adoption on Weighted Network

Efficient Data Transmission for Community Detection Algorithm Based on Node Similarity in Opportunistic Social Networks

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