Fast and Scalable Implementations of Influence Maximization Algorithms

2019 ◽  
Author(s):  
Marco Minutoli ◽  
Mahantesh Halappanavar ◽  
Ananth Kalyanaraman ◽  
Arun Sathanur ◽  
Ryan Mcclure ◽  
...  
Keyword(s):  
Influence Maximization ◽  
Maximization Algorithms

Summarizing social media content via bio-inspired influence maximization algorithms

2020 ◽  
Author(s):  
Christian Esposito ◽  
Vincenzo Moscato ◽  
Giancarlo Sperli ◽  
Chang Choi
Keyword(s):  
Social Media ◽  
Influence Maximization ◽  
Media Content ◽  
Maximization Algorithms

A numerical evaluation of the accuracy of influence maximization algorithms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hautahi Kingi ◽  
Li-An Daniel Wang ◽  
Tom Shafer ◽  
Minh Huynh ◽  
Mike Trinh ◽  
...  
Keyword(s):  
Numerical Evaluation ◽  
Influence Maximization ◽  
Maximization Algorithms

FSIM: A Fast and Scalable Influence Maximization Algorithm Based on Community Detection

2018 ◽  
Vol 26 (03) ◽  
pp. 379-396 ◽  
Author(s):  
Esmaeil Bagheri ◽  
Gholamhossein Dastghaibyfard ◽  
Ali Hamzeh
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Influence Maximization ◽  
Experimental Results ◽  
Maximization Problem ◽  
Maximization Algorithms ◽  
New Algorithms ◽  
Influence Maximization Problem

Influence maximization algorithms try to select a set of individuals in social networks that are more influential. The Influence maximization problem is important in marketing and many researchers has researched on it and proposed new algorithms. All proposed algorithms are not scalable and are very time consuming for very large social networks generally. In this paper, a fast and scalable influence maximization algorithm called FSIM is proposed based on community detection. FSIM algorithm decreases number of nodes that must be examined without loss of the operations quality therefore it can find seeds quickly. FSIM can maximize influence in large social networks. Experimental results show FSIM is faster and more scalable than existing algorithms.

scholarly journals Influence Maximization Algorithm Based on Reverse Reachable Set

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Gengxin Sun ◽  
Chih-Cheng Chen
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Cost Effective ◽  
Population Based ◽  
Influence Maximization ◽  
Reachable Set ◽  
Forward Algorithm ◽  
Influence Propagation ◽  
Propagation Function ◽  
Maximization Algorithms

Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS (dynamic-reverse reachable set) influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses an automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range but also greatly reduces the time complexity. The experimental results on the two real datasets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF (cost-effective lazy-forward) algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm, and pBmH (population-based metaheuristics) algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large-scale social network.

Influence Maximization on Social Networks: A Study

2019 ◽  
Vol 12 ◽  
Author(s):  
Shashank Sheshar Singh ◽  
Kuldeep Singh ◽  
Ajay Kumar ◽  
Bhaskar Biswas
Keyword(s):  
Theoretical Analysis ◽  
Algorithm Design ◽  
Influence Maximization ◽  
Future Research ◽  
Maximization Problem ◽  
Algorithmic Problem ◽  
Influence Spread ◽  
Design Perspective ◽  
Maximization Algorithms ◽  
Influence Maximization Problem

Background: Influence Maximization, which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Objective: In this paper, we give recent studies on influence maximization algorithms. The main goal of this survey is to provide recent studies and future research opportunities. We give taxonomy of influence maximization algorithms with the comparative theoretical analysis. Conclusion: This paper provides a theoretical analysis of influence maximization problem based on algorithm design perspective and also provides the performance analysis of existing algorithms.

Taxonomy of Influence Maximization Techniques in Unknown Social Networks

2021 ◽  
pp. 351-363
Author(s):  
B. Bazeer Ahamed ◽  
Sudhakaran Periakaruppan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Graph Mining ◽  
Influence Maximization ◽  
Business Strategies ◽  
The Social ◽  
Spread Of Influence ◽  
Maximization Algorithms ◽  
Real Challenge

Influence maximization in online social networks (OSNs) is the problem of discovering few nodes or users in the social network termed as ‘seed nodes', which can help the spread of influence in the network. With the tremendous growth in social networking, the influence exerted by users of a social network on other online users has caught the attention of researchers to develop effective influence maximization algorithms to be applied in the field of business strategies. The main application of influence maximization is promoting the product to a set of users. However, a real challenge in influence maximization algorithms to deal with enormous amount of users or nodes obtainable in any OSN is posed. The authors focused on graph mining of OSNs for generating ‘seed sets' using standard influence maximization techniques. Many standard influence maximization models are used for calculation of spread of influence; a novel influence maximization technique, namely the DegGreedy technique, has been illustrated along with experimental results to make a comparative analysis of the existing techniques.

scholarly journals Influence Maximization Algorithm Based on Reverse Reachable Set

2021 ◽  
Author(s):  
Gengxin Sun ◽  
Chih-Cheng Chen
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Real Data ◽  
Influence Maximization ◽  
Reachable Set ◽  
Data Sets ◽  
Influence Propagation ◽  
Independent Cascade Model ◽  
Propagation Function ◽  
Maximization Algorithms

Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range, and greatly reduce the time complexity. The experimental results on the two real data sets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm and pBmH algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large scale social network.

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