An Algorithm of Influence Maximization in Social Network Based on Local Structure Characteristics

2018 ◽  
pp. 403-412
Author(s):  
Yong Wang ◽  
Bohan Zhang ◽  
Jiahao Shi ◽  
Jing Yang ◽  
Jianpei Zhang
Keyword(s):  
Social Network ◽  
Local Structure ◽  
Influence Maximization ◽  
Structure Characteristics

scholarly journals An influence maximization method based on crowd emotion under an emotion-based attribute social network

2022 ◽  
Vol 59 (2) ◽  
pp. 102818
Author(s):  
Weimin Li ◽  
Yaqiong Li ◽  
Wei Liu ◽  
Can Wang
Keyword(s):  
Social Network ◽  
Influence Maximization ◽  
Crowd Emotion

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

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.

scholarly journals Individual and Social Network Structure Characteristics Associated with Peer Change Agent Engagement and Impact in a PrEP Intervention

2020 ◽  
Vol 24 (12) ◽  
pp. 3385-3394 ◽  
Author(s):  
Tim Walsh ◽  
John A. Schneider ◽  
Babak Mahdavi Ardestani ◽  
Lindsay E. Young
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Change Agent ◽  
Structure Characteristics ◽  
Social Network Structure

scholarly journals CBIM-RSRW: An Community-Based Method for Influence Maximization in Social Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 152115-152125
Author(s):  
Feng Cai ◽  
Lirong Qiu ◽  
Xinkai Kuai ◽  
Hongshuai Zhao
Keyword(s):  
Social Network ◽  
Influence Maximization ◽  
Community Based

AN IMPROVED EDGE-DIRECTED IMAGE INTERPOLATION ALGORITHM

2012 ◽  
Vol 12 (04) ◽  
pp. 1250023 ◽  
Author(s):  
XINGYUAN WANG ◽  
ZHIFENG CHEN ◽  
XUEMEI BAO
Keyword(s):  
Real Time ◽  
Local Structure ◽  
Time Complexity ◽  
Image Interpolation ◽  
Threshold Value ◽  
Subjective Quality ◽  
Experimental Results ◽  
Interpolation Algorithm ◽  
Visual Effects ◽  
Structure Characteristics

The paper sets forth an improved edge-directed image interpolation algorithm with low time complexity. The algorithm partitions images into homogeneous and edge areas by setting the preset threshold value based on the local structure characteristics. Specified algorithms are assigned to interpolate each classified areas respectively. The proposed method implements strategy in three steps to interpolate after setting the preset threshold value. In this way, it can achieve the goals of real-time interpolation and good subjective quality. Furthermore, the interpolated images have much more explicit edge regions and better visual effects using our proposed method than that of using other algorithms. Experimental results demonstrate that the method proposed by the authors is high-performed in image interpolation.

scholarly journals Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

2019 ◽  
Vol 11 (4) ◽  
pp. 95
Author(s):  
Wang ◽  
Zhu ◽  
Liu ◽  
Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Memory Effect ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Social Reinforcement ◽  
Reinforcement Effect ◽  
The Social ◽  
Influence Maximization Problem

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

scholarly journals Influence Maximization with Priority in Online Social Networks

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 183
Author(s):  
Canh V. Pham ◽  
Dung K. T. Ha ◽  
Quang C. Vu ◽  
Anh N. Su ◽  
Huan X. Hoang
Keyword(s):  
Social Network ◽  
Online Social Networks ◽  
Sampling Method ◽  
State Of The Art ◽  
Influence Maximization ◽  
Approximation Solution ◽  
Worst Case ◽  
New Approach ◽  
Influence Spread ◽  
Seed Collections

The Influence Maximization (IM) problem, which finds a set of k nodes (called seedset) in a social network to initiate the influence spread so that the number of influenced nodes after propagation process is maximized, is an important problem in information propagation and social network analysis. However, previous studies ignored the constraint of priority that led to inefficient seed collections. In some real situations, companies or organizations often prioritize influencing potential users during their influence diffusion campaigns. With a new approach to these existing works, we propose a new problem called Influence Maximization with Priority (IMP) which finds out a set seed of k nodes in a social network to be able to influence the largest number of nodes subject to the influence spread to a specific set of nodes U (called priority set) at least a given threshold T in this paper. We show that the problem is NP-hard under well-known IC model. To find the solution, we propose two efficient algorithms, called Integrated Greedy (IG) and Integrated Greedy Sampling (IGS) with provable theoretical guarantees. IG provides a 1−(1−1k)t-approximation solution with t is an outcome of algorithm and t≥1. The worst-case approximation ratio is obtained when t=1 and it is equal to 1/k. In addition, IGS is an efficient randomized approximation algorithm based on sampling method that provides a 1−(1−1k)t−ϵ-approximation solution with probability at least 1−δ with ϵ>0,δ∈(0,1) as input parameters of the problem. We conduct extensive experiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms in IM problem. The results indicate that our algorithm provides better solutions interns of influence on the priority sets when approximately give twice to ten times higher than threshold T while running time, memory usage and the influence spread also give considerable results compared to the others.

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