Maximizing the Cooperative Influence Spread in a Social Network Oriented to Viral Marketing

2016 ◽  
pp. 3-15 ◽  
Author(s):  
Hong Wu ◽  
Zhijian Zhang ◽  
Kun Yue ◽  
Binbin Zhang ◽  
Weiyi Liu
Keyword(s):  
Social Network ◽  
Viral Marketing ◽  
Influence Spread

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.

OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?

2018 ◽  
Vol 21 (06n07) ◽  
pp. 1850022 ◽  
Author(s):  
MEHRDAD AGHA MOHAMMAD ALI KERMANI ◽  
REZA GHESMATI ◽  
MASOUD JALAYER
Keyword(s):  
Genetic Algorithm ◽  
Social Network ◽  
Mathematical Programming ◽  
Real World ◽  
Programming Model ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Message Content ◽  
Mathematical Programming Model ◽  
The Real

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes’ opinions have not been considered. In this paper, a modified version of influence maximization which is named as “opinion-aware influence maximization” (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.

Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

2021 ◽  
Vol 15 (3) ◽  
pp. 1-21
Author(s):  
Guanhao Wu ◽  
Xiaofeng Gao ◽  
Ge Yan ◽  
Guihai Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Parallel Algorithms ◽  
Real World ◽  
Online Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Memory Overhead ◽  
Influence Spread ◽  
Real World Applications

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

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