Discovering influential users in micro-blog marketing with influence maximization mechanism

Influence Maximization in Social Networks using Deterministic Crowding Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2448.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 4258-4265

Keyword(s):

Social Network ◽

Information Diffusion ◽

Influence Maximization ◽

Maximization Problem ◽

Linear Threshold ◽

The Social ◽

Spread Of Influence ◽

Influential Users ◽

High Influence ◽

Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

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A quick GRASP-based method for influence maximization in social networks

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03510-4 ◽

2021 ◽

Author(s):

Isaac Lozano-Osorio ◽

Jesús Sánchez-Oro ◽

Abraham Duarte ◽

Óscar Cordón

Keyword(s):

Social Networks ◽

Social Network ◽

Local Search ◽

Computing Time ◽

Exact Algorithm ◽

Influence Maximization ◽

Computational Effort ◽

Viral Marketing ◽

Maximization Problem ◽

Influential Users

AbstractThe evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an $$\mathcal {NP}$$ NP -hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

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An Efficient Influence Maximization Algorithm to Discover Influential Users in Micro-blog

Web-Age Information Management - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08010-9_14 ◽

2014 ◽

pp. 113-124 ◽

Cited By ~ 3

Author(s):

Qian Ma ◽

Jun Ma

Keyword(s):

Influence Maximization ◽

Influential Users

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On Modeling Influence Maximization in Social Activity Networks under General Settings

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3451218 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-28

Author(s):

Rui Wang ◽

Yongkun Li ◽

Shuai Lin ◽

Hong Xie ◽

Yinlong Xu ◽

...

Keyword(s):

Online Social Networks ◽

Social Activity ◽

Influence Maximization ◽

Maximization Problem ◽

Computationally Efficient ◽

Activity Networks ◽

Online Activities ◽

Influential Users ◽

Real World Datasets ◽

The Impact

Finding the set of most influential users in online social networks (OSNs) to trigger the largest influence cascade is meaningful, e.g., companies may leverage the “word-of-mouth” effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various online activities, e.g., joining discussion groups and commenting on same pages or products, influence diffusion through online activities becomes even more significant. In this article, we study the impact of online activities by formulating social-activity networks which contain both users and online activities, and thus induce two types of weighted edges, i.e., edges between users and edges between users and activities. To address the computation challenge, we define an influence centrality via random walks, and use the Monte Carlo framework to efficiently estimate the centrality. Furthermore, we develop a greedy-based algorithm with novel optimizations to find the most influential users for node recommendation. Experiments on real-world datasets show that our approach is very computationally efficient under different influence models, and also achieves larger influence spread by considering online activities.

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