Time-sensitive Positive Influence Maximization in signed social networks

Wang, Yuejiao; Zhang, Yatao; Yang, Fei; Liu, Dong; Sun, Xin; Ma, Jun

doi:10.1016/j.physa.2021.126353

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…They also formulated an algorithm for knapsack seeding of the network that runs on each layer of the multiplex in parallel. Wang et al [ 24 ] proposed the time-sensitive positive influence maximization problem by considering two factors simultaneously, to select the seed node set that would achieve the maximum spreading of positive influence within a specified time limit. Furthermore, they constructed a heat diffusion-based polarity influence diffusion model and an improved k -step greedy seed node selection algorithm to solve the TP-IM problem.…”

Section: Related Workmentioning

confidence: 99%

Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking

Dai

Chen

et al. 2022

Entropy

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This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections.

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Section: Related Workmentioning

confidence: 99%