Maximum likelihood-based influence maximization in social networks

Liu, Wei; Li, Yun; Chen, Xin; He, Jie

doi:10.1007/s10489-020-01747-8

Cited by 10 publications

(4 citation statements)

References 43 publications

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“…Further, three different sampling strategies are proposed to construct the RR set to improve the performance. Liu et al [10] devised a maximum likelihood algorithm, which graded the existing graph according to the degree of nodes, and obtained seed nodes by likelihood calculation, avoiding Monte Carlo simulations. Li et al [11] presented an algorithm based on the Gaussian propagation model, which likens influence diffusion to the concentration of pollutants in space.…”

Section: Influence Maximizing In Traditional Social Networkmentioning

confidence: 99%

A Positive Influence Maximization Algorithm in Signed Social Networks

Zhu,

Huang,

Yang

et al. 2023

Computers, Materials &Amp; Continua

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The influence maximization (IM) problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network. The positive influence maximization (PIM) problem is an extension of the IM problem, which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread. To solve the PIM problem, this paper proposes the polar and decay related independent cascade (IC-PD) model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks. To overcome the low efficiency of the greedy based algorithm, this paper defines the polar reverse reachable (PRR) set and devises a signed reverse influence sampling (SRIS) algorithm. The algorithm utilizes the IC-PD model as well as the PRR set to select seeds. There are two phases in SRIS. One is the sampling phase, which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets. The other is the node selection phase, which uses a greedy coverage algorithm to select optimal seeds. Finally, Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness. Especially on the Slashdot dataset, SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

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Section: Influence Maximizing In Traditional Social Networkmentioning

confidence: 99%

A Positive Influence Maximization Algorithm in Signed Social Networks

Zhu,

Huang,

Yang

et al. 2023

Computers, Materials &Amp; Continua

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“…In Signed-PageRank, the dynamics of individuals’ beliefs and attitudes towards the advertisement are modeled based on recommendations from both positive and negative neighbors. (3) In recent years, there are existing approaches for identifying influential nodes using centrality measures as the centrality measure as degree discount centrality [ 37 ], k-shell centrality [ 38 ], the coreness centrality [ 39 ], Degree distance centrality [ 40 ], Initial multi-spreader nodes selection (IMSN) [ 41 ], Heuristic clustering [ 42 ], and Maximum Likelihood [ 43 ]. Most of this family’s approaches are based on structure information, with ignorance of the semantic aspect of the network, and even who used semantics, they used it lightly.…”

Section: Related Workmentioning

confidence: 99%

“…MLIM: [ 43 ] It uses maximum likelihood technology to find the top k influential nodes. It can avoid lots of simulation calculations to speed up the proposed algorithm.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Parallel social behavior-based algorithm for identification of influential users in social network

2021

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Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user’s interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.

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“…To sum up, reasonable measurement of social network users' influence is of great significance to improve the marketing effect, guide the healthy development of public opinion, and maintain the network order. This has aroused the interest of many researchers [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Group Decision Evaluation Model with Interval-Valued Intuitionistic Fuzzy Entropy Technology for Microblog User Influence

Zeng

Yang

Ren

et al. 2020

Wireless Communications and Mobile Computing

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Aiming at the shortcomings of some existing interval-valued intuitionistic fuzzy entropy, this paper proposes an interval-valued intuitionistic fuzzy entropy, which contains not only the interval distance between membership and nonmembership but also the average hesitancy information. On this basis, the impact evaluation of microblog users is studied. Through the multilevel decomposition of user influence, the coverage of microblog, user interaction, and user activity are constructed as the first level indicators, and nine secondary indicators are selected as the comprehensive evaluation index system of microblog influence. Considering the highly dynamic and unstructured characteristics of social network data, the idea of interval-valued intuitionistic fuzzy is introduced to transform the evaluation of social network users’ influence into interval-valued intuitionistic fuzzy multiattribute group decision-making problem. Finally, the model is applied to dynamic evaluation of Sina Weibo users’ influence to verify the effectiveness of the model.

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Maximum likelihood-based influence maximization in social networks

Cited by 10 publications

References 43 publications

A Positive Influence Maximization Algorithm in Signed Social Networks

A Positive Influence Maximization Algorithm in Signed Social Networks

Parallel social behavior-based algorithm for identification of influential users in social network

An Intelligent Group Decision Evaluation Model with Interval-Valued Intuitionistic Fuzzy Entropy Technology for Microblog User Influence

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