Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

2020 ◽  
Vol 30 (08) ◽  
pp. 1079-1096
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Scalable Algorithm ◽  
Influence Spread ◽  
Efficiency And Effectiveness ◽  
Spread Of Influence ◽  
Influential Nodes ◽  
Comparison Algorithms

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

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 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.

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.

A survey on meta-heuristic algorithms for the influence maximization problem in the social networks

Computing ◽  
2021 ◽  
Author(s):  
Zahra Aghaee ◽  
Mohammad Mahdi Ghasemi ◽  
Hamid Ahmadi Beni ◽  
Asgarali Bouyer ◽  
Afsaneh Fatemi
Keyword(s):  
Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Maximization Problem ◽  
The Social ◽  
Influence Maximization Problem

IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks

2014 ◽  
Vol 25 (1) ◽  
pp. 136-145 ◽  
Author(s):  
Xiaodong Liu ◽  
Mo Li ◽  
Shanshan Li ◽  
Shaoliang Peng ◽  
Xiangke Liao ◽  
...  
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization

scholarly journals Truss Decomposition using Triangle Graphs

2021 ◽  
Author(s):  
Mohsen Rezvani ◽  
Mojtaba Rezvani
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
State Of The Art ◽  
Decomposition Algorithms ◽  
Scalable Algorithm ◽  
Large Scale Network ◽  
Triangle Graph ◽  
Speed Up ◽  
Scale Network ◽  
High Degree

Abstract Recent studies have shown that social networks exhibit interesting characteristics such as community structures, i.e., vertexes can be clustered into communities that are densely connected together and loosely connected to other vertices. In order to identify communities, several definitions have been proposed that can characterize the density of connections among vertices in the networks. Dense triangle cores, also known as $k$-trusses, are subgraphs in which every edge participates at least $k-2$ triangles (a clique of size 3), exhibiting a high degree of cohesiveness among vertices. There are a number of research works that propose $k$-truss decomposition algorithms. However, existing in-memory algorithms for computing $k$-truss are inefficient for handling today’s massive networks. In this paper, we propose an efficient, yet scalable algorithm for finding $k$-trusses in a large-scale network. To this end, we propose a new structure, called triangle graph to speed up the process of finding the $k$-trusses and prove the correctness and efficiency of our method. We also evaluate the performance of the proposed algorithms through extensive experiments using real-world networks. The results of comprehensive experiments show that the proposed algorithms outperform the state-of-the-art methods by several orders of magnitudes in running time.

scholarly journals A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehrdad Agha Mohammad Ali Kermani ◽  
Reza Ghesmati ◽  
Mir Saman Pishvaee
Keyword(s):  
Social Networks ◽  
Robust Optimization ◽  
Optimization Model ◽  
Text Messaging ◽  
Influence Maximization ◽  
Optimization Approach ◽  
Expected Number ◽  
Influential Nodes ◽  
Heterogeneous Nodes ◽  
Probabilistic Nature

AbstractInfluence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

scholarly journals TSSCM: A synergism-based three-step cascade model for influence maximization on large-scale social networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0221271
Author(s):  
Xiaohui Zhao ◽  
Fang’ai Liu ◽  
Shuning Xing ◽  
Qianqian Wang
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Influence Maximization ◽  
Cascade Model
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