Viral marketing with positive influence

Zhang, Zhao; Shi, Yishuo; Willson, James K.V.; Du, Ding‐Zhu; Tong, Guangmo

doi:10.1109/infocom.2017.8057070

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…A minimal positive propagation model [26] in which more than half of a node's neighbours are active is proposed, and then, the node will be activated. Yang [27] et al studied the relative influence maximization (RIM) problem, and in this research, the optimal positive seed set was selected by considering the presence of negative influence.…”

Section: Related Workmentioning

confidence: 99%

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Dai

2021

IEEE Access

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With the advancement of science and technology, research on influence maximization in networks has become a hot spot. In social networks, there is not only a positive relationship among nodes but also a negative impact, and the negative impact often plays a greater role than the positive impact, as observed for shopping websites or online votes. This paper proposes a method based on an independent cascade model by emphasizing the negative impact in symbolic networks to solve the problem of influence maximization. First, an algorithm that is based on an independent path and that emphasizes negative influence is designed to obtain the probability among nodes. Based on the activation probability, an algorithm is proposed to identify nodes that could have the greatest impact on the influence increment from the seeds. Finally, the seed set is confirmed based on the influence in the corresponding symbol network. In the experiment performed on real-world network data, the result indicate that the proposed algorithm causes more substantial influence propagation than do other algorithms.

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Section: Related Workmentioning

confidence: 99%

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Dai

2021

IEEE Access

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“…Note that equation ( 4) can be seen as a variation of the IM problem 29 in social networks. However, solutions for the IM problem cannot be used to solve our problem directly due to the following reasons: Different from social networks with the relatively stable topology, the topology of VANETs changes constantly as vehicles move at high speed and vehicles get disconnected frequently.…”

Section: Lemma 1 B(s) Is a Submodular Functionmentioning

confidence: 99%

“…It can achieve ( 1 − 1 / e − ε ) approximation through theoretical analysis. The work in Zhang et al 29 aimed at a Partial Positive Influence Seeding (PPIS) problem which is a deformation of IM problem. They proved that the function of PPIS is a nonsubmodular function and presented an algorithm with an O ( ( log n ) 2 H ( ⌊ pn ⌋ ) ) approximation.…”

Section: Related Workmentioning

confidence: 99%

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An advertisement dissemination strategy with maximal influence for Internet-of-Vehicles

Zhao

Zhang

et al. 2019

International Journal of Distributed Sensor Networks

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The recent advances in computation and communication technologies have led to the emergence of Internet-of-Vehicles where vehicles are connected to each other through sensors so that they can exchange information to improve driving safety, efficiency, and comfort. Internet-of-Vehicle has attracted attention from both academia and industry, as it promises huge commercial and research value. This article studies an advertisement dissemination problem in Internet-of-Vehicle with an aim to maximize the profit (number of vehicles to receive advertisements) given a limited budget. In this problem, advertisements will be first sent to a selected set of seed vehicles, then forwarded to neighboring vehicles. To find the influential set of vehicles, we examine the probability of interaction between vehicles by exploiting their mobility. In particular, we present the computation of node marginal gain in four cases by examining vehicle connectivity and topology. Simulation results demonstrate that the proposed algorithm outperforms existing methods for influence maximization by running time and deliver ratio under different traffic scenarios.

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“…[6]. At present, there are various studies that offer a solution to this problem using multiple techniques [7]. Majority of such problems are solved using directed graph theory that depends on vertices, edges, the weight assigned to edges mainly [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Framework for Computing Potential Node with Higher Influence in Complex Social Network

Kuamr S*,

2020

IJRTE

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The increasing data complexity in social network imposes challenges towards carrying out investigation towards analytics. Similar scenario has started evolving in social network most recently owing to the usage of ubiquitous devices which gives rise to accumulation of complex set of data that offers potential challenge to understand the potential node. Identification of potential node is an essential demand to understand the role player in social network. Therefore, this paper presents a novel analytical methodology to construct a discrete community with higher precision along with a novel progressive algorithm for identifying the potential node that offers a higher degree of influence in social network. Dynamic optimization, as well as probability theory, has been used in order to perform modeling of the proposed system. Along with an effective computational performance, the comparative analysis shows that proposed system offers better performance in contrast to existing techniques with respect to influence degree and information processing time. The inferencing of the quantified outcome status shows that the formulated approach attains ~70% performance improvement in the case of minimizing the processing time as compared to the Chen approach. The identification of potential node in four different types of networks have also significantly improved with negligible computational effort.

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Viral marketing with positive influence

Cited by 10 publications

References 17 publications

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Influence Maximization in Signed Networks by Enhancing the Negative Influence

An advertisement dissemination strategy with maximal influence for Internet-of-Vehicles

Framework for Computing Potential Node with Higher Influence in Complex Social Network

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