Results 11 to 20 of about 36,296 (256)
A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks
Applied Sciences, 2022 Nowadays, we use social networks such as Twitter, Facebook, WeChat and Weibo as means to communicate with each other. Social networks have become so indispensable in our everyday life that we cannot imagine what daily life would be like without social ...
Luodi Xie, Huimin Huang, Qing Du
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Stability of influence maximization [PDF]
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 Erratum of Paper "Stability of Influence Maximization" which was presented and published in the ...
Xinran He, David Kempe 0001
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Influence Learning and Maximization [PDF]
, 2021 The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximization algorithms that identify the right nodes to lockdown for epidemic containment, hire for viral marketing campaigns, block for
Panagopoulos, George +1 more
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Adversarial Influence Maximization [PDF]
2019 IEEE International Symposium on Information Theory (ISIT), 2019 We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influence process, such that the number of influenced nodes at the conclusion of the campaign is as large as possible.
Justin Khim, Varun S. Jog, Po-Ling Loh
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Online Influence Maximization [PDF]
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015 Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or seed nodes), with the hope that they will convince their friends to buy it. One way to formalize this objective is through the problem of influence maximization (or IM), whose goal is to find the ...
Lei, Siyu +4 more
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Influence Maximization in Hypergraphs
CoRR, 2022 Influence maximization in complex networks, i.e., maximizing the size of influenced nodes via selecting K seed nodes for a given spreading process, has attracted great attention in recent years. However, the influence maximization problem in hypergraphs, in which the hyperedges are leveraged to represent the interactions among more than two nodes, is ...
Ming Xie +3 more
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Overexposure-Aware Influence Maximization [PDF]
ACM Transactions on Internet Technology, 2020 Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the ...
Grigorios Loukides +2 more
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Maximization influence in dynamic social networks and graphs
Array, 2022 Social influence and influence diffusion have been extensively studied in social networks. However, most existing works on influence diffusion focus on static networks.
Gkolfo I. Smani +1 more
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Influence Maximization Based on Backward Reasoning in Online Social Networks
Mathematics, 2021 Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion.
Lin Zhang, Kan Li
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Influence maximization: Divide and conquer
Physical Review E, 2023 The problem of influence maximization, i.e., finding the set of nodes having maximal influence on a network, is of great importance for several applications. In the past two decades, many heuristic metrics to spot influencers have been proposed. Here, we introduce a framework to boost the performance of any such metric.
Siddharth Patwardhan +2 more
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