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A Modified Degree Discount Heuristic for Influence Maximization in Social Networks

Roaa Aldawish, Heba Kurdi
2020 Procedia Computer Science  
Many studies have proposed influence maximization heuristics, such as degree and degree-discount algorithms.  ...  Many studies have proposed influence maximization heuristics, such as degree and degree-discount algorithms.  ...  Complexity in the greedy algorithm refers to the computation of the influence spread for each variation of the initial seed set, so the heuristic algorithm was proposed to reduce the time complexity and  ... 
doi:10.1016/j.procs.2020.03.045 fatcat:pbpf4qzcfndqzjdsucnyig57ay


Suqi Cheng, Huawei Shen, Junming Huang, Guoqing Zhang, Xueqi Cheng
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks.  ...  guaranteed in all conventional greedy algorithms in the literature of influence maximization.  ...  Table 1 : 1 Time and space complexity of algorithms Algorithms Time complexity Space complexity Table 2 : 2 Statistics of six test real world networks. Datasets #Nodes #Edges Directed?  ... 
doi:10.1145/2505515.2505541 dblp:conf/cikm/ChengSHZC13 fatcat:7rdgapm3uzbmhmj6yidhol6xci

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

Wang, Zhu, Liu, Wang
2019 Future Internet  
Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence.  ...  In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/fi11040095 fatcat:oyp46sc7qvh57ek4qtl3pwlcvm

Relative influence maximization in competitive social networks

Dingda Yang, Xiangwen Liao, Huawei Shen, Xueqi Cheng, Guolong Chen
2017 Science China Information Sciences  
In this article, we study the relative influence maximization (RIM) problem, which seeks to select initial individuals as a positive seed set under the existence of negative individuals, maximizing the  ...  difference between the spread of positive opinions and the spread of negative opinions, i.e., the relative influence.  ...  ., the greedy positive influence maximization algorithm (GreedyPIM) and the greedy negative influence minimization algorithm (GreedyNIM).  ... 
doi:10.1007/s11432-016-9080-3 fatcat:otb5ire555cf5nwjgcxvpdoova

Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks [chapter]

Doina Bucur, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, Alberto Tonda
2017 Lecture Notes in Computer Science  
The methodology is tested on two real-world case studies, using two different influence propagation models, and compared against state-of-the-art heuristic algorithms.  ...  The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation.  ...  Future works will also focus on hybrid techniques, developing memetic algorithms [17] for influence maximization, which may be able to extract -and combine-the best qualities of EAs and heuristics.  ... 
doi:10.1007/978-3-319-55849-3_15 fatcat:4gfkncif6vhq5eopqedeomj3za

Efficient influence maximization in social networks

Wei Chen, Yajun Wang, Siyu Yang
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence.  ...  One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves influence spread  ...  CONCLUDING REMARKS In this paper, we propose the efficient algorithms and heuristics for the influence maximization problem.  ... 
doi:10.1145/1557019.1557047 dblp:conf/kdd/ChenWY09 fatcat:ejvr3f4zr5drjcwxqbs42l3dr4

Selection of top-K influential users based on radius-neighborhood degree, multi-hops distance and selection threshold

Mohammed Alshahrani, Fuxi Zhu, Lin Zheng, Soufiana Mekouar, Sheng Huang
2018 Journal of Big Data  
We, therefore, propose an analysis of time complexity of the proposed algorithms and show its worst time complexity.  ...  Influence maximization in the social network becomes increasingly important due to its various benefit and application in diverse areas.  ...  Availability of data and materials All datasets used are open source and available online.  ... 
doi:10.1186/s40537-018-0137-4 fatcat:pn4mb33b5ngnhf6lf2kn5dongq

A Balanced Method for Budgeted Influence Maximization

Xinhui Xu, Yong Zhang, Qingcheng Hu, Chunxiao Xing
2015 Proceedings of the 27th International Conference on Software Engineering and Knowledge Engineering  
Finding how the influence spreads and maximizing influence spread within OSNs have been extensively studied.  ...  To solve these problems we firstly propose PageRank Based Cost (PRBC) model to assess the cost of nodes in OSN according to their importance (influence); secondly we present Budgeted Random Maximal Degree  ...  We apply IC model to BRMDN and compare the results with some state-of-the-art heuristic based influence maximization algorithms.  ... 
doi:10.18293/seke2015-157 dblp:conf/seke/XuZHX15 fatcat:ug73pvddhzct5lorct7v35pfe4

A Survey on Influence Maximization in a Social Network [article]

Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar
2018 arXiv   pre-print
Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network?  ...  This problem is known as Target Set Selection in a social network (TSS Problem) and more popularly, Social Influence Maximization Problem (SIM Problem).  ...  Based on the OCI Model, they formulated the maximizing effective opinion problem and proposed two fast and scalable heuristics, namely Openion Spread Influence Maximization (OSIM) and EaSyIm having the  ... 
arXiv:1808.05502v1 fatcat:3n7abvpufng7nptkdnm2fb5uqi

Influence Maximization Algorithm Based on Reverse Reachable Set

Gengxin Sun, Chih-Cheng Chen, Isabella Torcicollo
2021 Mathematical Problems in Engineering  
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.  ...  of reverse reachable sets, which not only obtains a better influence propagation range but also greatly reduces the time complexity.  ...  of the high time complexity of the greedy algorithm and also solves the problem that the heuristic algorithm lacks theoretical guarantee and cannot obtain the optimal solution.  ... 
doi:10.1155/2021/5535843 fatcat:4ch26plfhnebpbcox67wpss3ou

Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model [chapter]

Xinran He, Guojie Song, Wei Chen, Qingye Jiang
2012 Proceedings of the 2012 SIAM International Conference on Data Mining  
We call this problem the influence blocking maximization (IBM) problem.  ...  We conduct extensive simulations of CLDAG, the greedy algorithm, and other baseline algorithms on real-world and synthetic datasets.  ...  Acknowledgement This work is supported by the National Natural Science Foundation of China (60703066, 60874082), and Beijing Municipal Natural Science Foundation (4102026).  ... 
doi:10.1137/1.9781611972825.40 dblp:conf/sdm/HeSCJ12 fatcat:xhw3k3nhcfdjxjeubcivraksgy

Application of the Ant Colony Optimization Algorithm to the Influence-Maximization Problem

Wan-Shiou Yang, Shi-Xin Weng
2012 International Journal of Swarm Intelligence and Evolutionary Computation  
Consumers often form complex social networks based on a multitude of different relations and interactions.  ...  These interactions influence the decisions they make about adopting products or behaviors, and hence a company could receive a large cascade of further recommendations if it can identify and target influential  ...  Conclusions This research used the search capacity of the ACO algorithm to solve the influence-maximization problem in both noncompetitive and competitive cases.  ... 
doi:10.4303/ijsiec/235566 fatcat:q7ejlqsigfhezeqq3dmx4zikje

Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model Technical Report [article]

Xinran He, Guojie Song, Wei Chen, Qingye Jiang
2011 arXiv   pre-print
We call this problem the influence blocking maximization (IBM) problem.  ...  We conduct extensive simulations of CLDAG, the greedy algorithm, and other baseline algorithms on real-world and synthetic datasets.  ...  We compare the performance of CLDAG with the greedy algorithm and other heuristic algorithms.  ... 
arXiv:1110.4723v1 fatcat:mjza7wlimvcp5ou6fqypehmgvy

Influence Maximization Based on Backward Reasoning in Online Social Networks

Lin Zhang, Kan Li
2021 Mathematics  
Although heuristic algorithms can improve efficiency, it is at the expense of accuracy.  ...  Influence maximization is one of the hot research issues in online social network analysis.  ...  [10] discussed the relationship between influence diffusion and the degree of nodes and proposed the Degree-Discount heuristic algorithm.  ... 
doi:10.3390/math9243189 fatcat:hdskecamrrbqvadbx2ucsys4za

A New Greedy Algorithm For Influence Maximization On Signed Social Networks

Aybike Şimşek
2019 Gazi Mühendislik Bilimleri Dergisi  
We compared the EGA's performance on 2 public datasets with random seed selection, out degree heuristic, and one stateof-the-art greedy algorithm.  ...  The IM problem aims to maximize the spread of an influence (e.g. an opinion, an advertisement) in a social network by using a small number of the most effective individuals, whom is called influencers.  ...  ., have suggested another discount algorithm called CascadeDiscount that reduces time complexity of greedy algorithm to solve the IM problem [13] .  ... 
doi:10.30855/gmbd.2019.03.06 fatcat:ufc7tyd6xrbe3oaq3o52rsmxmm
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