A new viral marketing strategy with the competition in the large-scale online social networks

Pham, Canh V.; Ha, Dung K. T.; Ngo, Dung Q.; Vu, Quang C.; Hoang, Huan X.

doi:10.1109/rivf.2016.7800260

Cited by 10 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experiment, the influence propagation of the EN-IM algorithm was tested and compared with that of five other algorithms, Random, MaxDegree [40], Effective Degree [14], Greedy [41], MIA-N [42]and PIMSN [43].…”

Section: A Data Sets and The Comparison Algorithmsmentioning

confidence: 99%

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Dai

2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: A Data Sets and The Comparison Algorithmsmentioning

confidence: 99%

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Dai

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Greedy: In each step of this method [34], the influence spreading increment of each node is estimated by Monte-Carlo simulations. The node which has the highest influence spreading increment will be selected and added to the seed set.…”

Section: Framework Of the Algorithm Imuu_rwmentioning

confidence: 99%

A Random Walk Approach for Avoiding Unwanted Users in Competitive Social Network

Liu

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

How to detect influential customers to broadcast advertisements to the maximum range is a key issue in effective viral marketing. If multiple companies sell the same product in viral marketing campaigns, there was competition between them. There may exist some unwanted users who hold hostile opinions, and these users may have a negative effect upon receiving promotional information. The company does not want the advertising information to reach such unwanted users over a period of time. In such competitive advertising, how to propagate the positive influence of its product and avoid it reaching the unwanted users of competitors in the limited time is a critical problem for business product promotion. Motivated by the phenomenon, we study the influence maximization problem with limited unwanted users (IML) in the independent cascade (IC) model. To accelerate the process of the influence propagation simulation, we present a path sampling approach based on the random walk to simulate the process of influence propagation. To avoid the unwanted users, only the influences on the paths reaching the wanted users are calculated at each time step, and the influences reaching the unwanted users are ignored.To reduce the computation time, the paths of the random walk will be recorded to avoid repeated random walks in the subsequent seed selection. To find the optimal influential customers, we employ a greedy scheme to select the top-k most influential nodes as seed nodes. Experimental results over the real-world datasets show that the algorithm we presented can get wider effective influence spreading than other algorithms.

show abstract

“…First, both the INCIM and IPA algorithms use the idea of communities to find influential nodes and like LDAG have reasonable running times and find good quality nodes. Second, though the High-Degree and greedy approximation algorithms are now almost 15 years old and may thus appear to be obsolete, they are still very commonly used in comparisons involving recently-proposed approaches to competitive and non-competitive influence maximization [11][12][13]38,39] .…”

Section: Experiments Setupmentioning

confidence: 99%