SMG: Fast scalable greedy algorithm for influence maximization in social networks

Heidari, Mehdi; Asadpour, Masoud; Faili, Hesham

doi:10.1016/j.physa.2014.10.088

Cited by 50 publications

(17 citation statements)

References 8 publications

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“…Its complexity is O ( N 2 ). State Machine Greedy (SMG) [ 27 ] is considered a fast and scalable a greedy algorithm. It records the already evaluated influence propagation of i nodes as well as the final state, acting as a single state machine.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

2021

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“…Another greedy algorithm named SM G which stands for State-Machine Greedy was proposed recently by M. Heidari et al [107]. The main idea improves the speed of greedy algorithms by preventing recalculation done by older methods.…”

Section: 3mentioning

confidence: 99%

Influence analysis: A survey of the state-of-the-art

Han¹,

Li²

2018

Mathematical Foundations of Computing

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Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference. 201 202 MENG HAN AND YINGSHU LIachieved. Even so, prior to the Internet, quantitative data of social networks were scanty and the further influence analysis in social networks was in the slow-lane. In 2007, Nicholas et al. [47] published their years of research results based on the historical data from the spreading of obesity over 32 years. From the same research filed, David et al. [11] proposed another idea about the spread of obesity in social networks based on the simulations which further considered the group effect in obesity spreading. Both works tried to explore how the influence diffusion in social networks affects obesity. In their model, individuals' influence over each other rely on food intake and physical activities [70]. Since other models consider obesity as a "contagious" phenomenon that can be caught if most social contracts are deemed obese, the interaction of social networks with environmental factors could not be explored. It was not accounted for in the general model where the social networks were proposed as a means to mitigate the obesity epidemic. Many other research results have been obtained recently, such as smoking behavior [48][52], happiness [68], and loneliness [25][117] which spread along a social network over time.At an American high school, Salath et al.[207] obtained high-resolution data of close proximity interactions during a typical day, and their work helps with the reconstruction of a social network for infectious disease transmission by using wireless sensor network technology [246,98]. Through simulations, they showed that targeted immunization using the contact-network data is much more effective than random immunization. Stehle et al. [216] report a similar result like Salath [207] in a French primary school. The team headed by Stehle also provided several public-health implications of infectious diseases by collecting a period of history data from their experiments. By analyzing the real experiments in two ...

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“…This problem simply aims at identifying the minimal set of influencers that, if influenced, would lead to the largest contagion in the network [12]. Previous research selected influencers based on the overall network structure [8], [14], [22], [23]. Using this approach, high IDCF is accrued, and the information might not reach the intended users.…”

Section: B Information Diffusion and The Influencementioning

confidence: 99%

“…The heuristic algorithms depend on efficient social network metrics, such as the centrality measures and K-shell, [2], [26], [5]. This approach is fast [23] but has low influencer identification, and influence spread [14], [23] and does not identify weak nodes as potential influencers.…”

Section: B Information Diffusion and The Influencementioning

confidence: 99%

“…In doing this, the influencers need to be identified in the overall network structure. This procedure further leads to the influencer identification problem, which has generated a lot of research in recent years [13], [14], and more recently towards influencing a set of target users [7], [15]. A high diffusion cost function is good in scenarios where the information is aimed at the general population [8], while the reverse is the case concerning the target population.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence Maximization Towards Target Users on Social Networks for Information Diffusion

Olanrewaju

Ahmad

Mahmudin

2017

Recent Trends in Information and Communication Technology

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Influence maximisation has been an area of active research in recent years. This study aims to extend the fundamental influence maximisation problem (IMP) with respect to a set of target users on a social network. It is important to aim at the target users to speed up the rate of information diffusion and reduce the information diffusion cost. In doing so, the MITU algorithm was formulated and compared with state of the art algorithms. Publicly available datasets were used in validating the proposed algorithm. It was found that the MITU identified all target nodes while significantly lowering the information diffusion cost function (IDCF) by up to 79%. The influence overlap problem was equally identified in the heuristic algorithm where the seed set size was reduced by an average of six times. Furthermore, the random influencer selection identifies target nodes better than the betweenness and PageRank centralities. The findings could help organisations to reach target users on social media in the shortest cycle.

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SMG: Fast scalable greedy algorithm for influence maximization in social networks

Cited by 50 publications

References 8 publications

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

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

Influence analysis: A survey of the state-of-the-art

Influence Maximization Towards Target Users on Social Networks for Information Diffusion

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