Positive Influence Maximization in the Signed Social Networks Considering Polarity Relationship and Propagation Probability

2021 ◽  
Vol 31 (02) ◽  
pp. 249-267
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Jinfeng Yu
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Positive Influence ◽  
Vital Role ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Propagation Model ◽  
Maximization Problem ◽  
Signed Social Networks ◽  
Independent Cascade

The purpose of influence maximization problem is to select a small seed set to maximize the number of nodes influenced by the seed set. For viral marketing, the problem of influence maximization plays a vital role. Current works mainly focus on the unsigned social networks, which include only positive relationship between users. However, the influence maximization in the signed social networks including positive and negative relationships between users is still a challenging issue. Moreover, the existing works pay more attention to the positive influence. Therefore, this paper first analyzes the positive maximization influence in the signed social networks. The purpose of this problem is to select the seed set with the most positive influence in the signed social networks. Afterwards, this paper proposes a model that incorporates the state of node, the preference of individual and polarity relationship, called Independent Cascade with the Negative and Polarity (ICWNP) propagation model. On the basis of the ICWNP model, this paper proposes a Greedy with ICWNP algorithm. Finally, on four real social networks, experimental results manifest that the proposed algorithm has higher accuracy and efficiency than the related methods.

Net positive influence maximization in signed social networks

2021 ◽  
pp. 1-12
Author(s):  
Dong Li ◽  
Yuejiao Wang ◽  
Muhao Li ◽  
Xin Sun ◽  
Jingchang Pan ◽  
...  
Keyword(s):  
Social Networks ◽  
Greedy Algorithm ◽  
Seed Set ◽  
Social Systems ◽  
Research Direction ◽  
Negative Influence ◽  
Positive Influence ◽  
Influence Maximization ◽  
The Real ◽  
Signed Social Networks

In the real world, a large number of social systems can be modeled as signed social networks including both positive and negative relationships. Influence maximization in signed social networks is an interesting and significant research direction, which has gained some attention. All of existing studies mainly focused on positive influence maximization (PIM) problem. The goal of the PIM problem is to select the seed set with maximum positive influence in signed social networks. However, the selected seed set with maximum positive influence may also has a large amount of negative influence, which will cause bad effects in the real applications. Therefore, maximizing purely positive influence is not the final and best goal in signed social networks. In this paper, we introduce the concept of net positive influence and propose the net positive influence maximization (NPIM) problem for signed social networks, to select the seed set with as much positive influence as possible and as less negative influence as possible. Additionally, we prove that the objective function of NPIM problem under polarity-related independent cascade model is non-monotone and non-submodular, which means the traditional greedy algorithm is not applicable to the NPIM problem. Thus, we propose an improved R-Greedy algorithm to solve the NPIM problem. Extensive experiments on two Epinions and Slashdot datasets indicate the differences between positive influence and net positive influence, and also demonstrate that our proposed solution performs better than the state-of-the-art methods in terms of promoting net positive influence diffusion in less running time.

scholarly journals Influence maximization in social networks

2019 ◽  
Author(s):  
◽  
Ghinwa Bou Matar
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Influence Propagation ◽  
Influence Model ◽  
Main Challenge ◽  
Influence Spread ◽  
Total Influence

The main challenge in viral marketing, that is powered by social networks, is to minimize the seed set that will initiate the diffusion process and maximize the total influence at its termination. The aim of this thesis is to study influence propagation models under the influence maximization problem and to investigate the effectiveness of a new model that is based on a multi-objective approach. We propose a Depth-Based Diminishing Influence model (DBDM) that is based on adding nodes to the seed set by considering influenced in-neighbors and how far these in-neighbors are from the initial activated set. As an enhancement to our approach, we used a clustering mechanism to help increase the influence spread. Several experiments were conducted to compare between our approach and previous work. As a result, the selection of the seed set under the DBDM model boosted the influence spread substantially compared to previously proposed models.

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 A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

Positive influence maximization in signed social networks based on simulated annealing

2017 ◽  
Vol 260 ◽  
pp. 69-78 ◽  
Author(s):  
Dong Li ◽  
Cuihua Wang ◽  
Shengping Zhang ◽  
Guanglu Zhou ◽  
Dianhui Chu ◽  
...  
Keyword(s):  
Social Networks ◽  
Simulated Annealing ◽  
Positive Influence ◽  
Influence Maximization ◽  
Signed Social Networks

Time-sensitive Positive Influence Maximization in signed social networks

2021 ◽  
pp. 126353
Author(s):  
Yuejiao Wang ◽  
Yatao Zhang ◽  
Fei Yang ◽  
Dong Li ◽  
Xin Sun ◽  
...  
Keyword(s):  
Social Networks ◽  
Positive Influence ◽  
Influence Maximization ◽  
Signed Social Networks

scholarly journals A novel approach for fast and effective influence spread maximization in social networks

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Computational Time ◽  
Maximization Problem ◽  
Influence Spread ◽  
The Social ◽  
Spread Of Influence ◽  
Influence Maximization Problem

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

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

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.

scholarly journals On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaodong Liu ◽  
Xiangke Liao ◽  
Shanshan Li ◽  
Si Zheng ◽  
Bin Lin ◽  
...  
Keyword(s):  
Social Networks ◽  
Real World ◽  
Search Space ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Dynamic Social Networks ◽  
Influence Spread ◽  
The Impact ◽  
Influential Individuals

Influence maximization problem aims to identify the most influential individuals so as to help in developing effective viral marketing strategies over social networks. Previous studies mainly focus on designing efficient algorithms or heuristics on a static social network. As a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time. In this paper, we propose an incremental approach, IncInf, which can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to narrow the search space into nodes experiencing major increases or with high degrees. To evaluate the efficiency and effectiveness, we carried out extensive experiments on real-world dynamic social networks: Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static algorithm, IncInf achieves remarkable speedup in execution time while maintaining matching performance in terms of influence spread.

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