An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks

Jiang, Chenting; D'Arienzo, Anthony; Li, Weihua; Wu, Shiqing; Bai, Quan

doi:10.23919/jsc.2021.0007

Cited by 10 publications

(5 citation statements)

References 42 publications

(41 reference statements)

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“…Furthermore, they gave a social question-answering website knowledge dissemination network inference method. Based on the linear threshold model and value cocreation theory, the author [14] proposed a social network communication and corporate value cocreation strategy model for the characteristics of negative word-of-mouth.…”

Section: Information Dissemination Modelmentioning

confidence: 99%

Influential Incremental Learning-Based Privacy Preservation for Social Network Information

Alshudukhi

2022

Security and Communication Networks

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Social network influence dissemination focuses on employing a small number of seed sets to generate the most significant possible influence in social networks and considers forwarding to be the only technique of information transmission, ignoring all other ways. Users, for example, can post a message via this mode of distribution (called para), which is difficult to trace, posing a danger of privacy leakage. This research tries to address the aforementioned issues by developing a social network information transmission model that supports the paranormal relationship. It suggests a way of disseminating information called Local Greedy, which aids in the protection of user privacy. Its effect helps to reconcile the conflict between privacy protection and information distribution. Aiming at the enumeration problem of seed set selection, an incremental strategy that supports privacy protection is proposed to construct seed sets to reduce time overhead; a local influence subgraph method of computing nodes is given to estimate the influence of seed set propagation quickly; the group satisfies the constraints of privacy protection, and a plan is proposed to deduce the upper limit of the probability of node leakage state, avoiding the time cost of using the Monte Carlo method using the crawled Sina Weibo dataset. Experimental verification and example analysis are carried out, and the results show the effectiveness of the proposed method.

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Section: Information Dissemination Modelmentioning

confidence: 99%

Influential Incremental Learning-Based Privacy Preservation for Social Network Information

Alshudukhi

2022

Security and Communication Networks

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“…However, many authors are already convinced that complex networks represent an ideal framework to investigate organized crime 1,[3][4][5][6] . In line with other research on social systems [7][8][9][10][11] , complex networks can suitably describe the intricate relations among criminals and reveal the patterns based on which criminal organizations operate. Beyond theoretical explorations, recent articles have empirically demonstrated that these methods can be useful in investigations involving drug trafficking 12 , political networks 13,14 , police intelligence networks 15 , cartel detection 16 , money laundering 17 , pedophile rings 18 , and a range of other examples [19][20][21][22][23][24] .…”

Section: Introductionmentioning

confidence: 67%

Machine learning partners in criminal networks

Lopes

Cunha

Martins

et al. 2022

Sci Rep

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Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.

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“…HE advancement of internet technology in recent years has promoted the rapid development of Social Internet of Things (SIoT) [1][2][3][4][5][6], which is a concept proposed through the combination of social networks [7][8][9] and Internet of Things (IoT) [10][11][12]. The attributes of social networks are introduced into the IoT to establish relationships between things-things, things-people, and people-people, so as to realize the connection, service, and application of the IoT.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Edge Collaboration Dynamics Information Dissemination Model for Social Internet of Things

Zhang

Zou

Pan

2023

IEEE Trans. Netw. Sci. Eng.

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Studying the mechanism of information dissemination in Social Internet of Things (SIoT) has practical significance for real-time information control and scientific management decision-making in real world. Aiming at the problem of how to realize the information interaction between people, people and things, and things and things in SIoT, we propose a cloud-edge collaborative dynamic information dissemination model (CCDIDM) for SIoT. Firstly, considering the interactive influence of information dissemination between individual users, between IoT devices, and between users and devices, the coupling relationship between nodes is established. Based on the theory of dissemination dynamics, the information propagation process between coupled nodes of SIoT is analyzed, and the efficient interactive dissemination of information is simulated. Theoretical analysis of the model is carried out, and the information dissemination threshold and the stability of the equilibrium point are deduced. Simulation results are consistent with the theoretical analysis, demonstrating that CCDIDM can describe information dissemination. Simultaneously, it was found that, stronger the perception awareness of individual users, smaller the scale of information dissemination. In addition, the influence of various parameters on the scale of information dissemination is verified. Adjusting the size of such parameters can promote or inhibit the dissemination of information.

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An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks

Cited by 10 publications

References 42 publications

Influential Incremental Learning-Based Privacy Preservation for Social Network Information

Influential Incremental Learning-Based Privacy Preservation for Social Network Information

Machine learning partners in criminal networks

Cloud-Edge Collaboration Dynamics Information Dissemination Model for Social Internet of Things

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