Jun 24, 2021 · We consider a Markov chain model of influence in a network where inactive nodes can be activated by their active in-neighbors or by external ...
Here, we extend its modeling capabilities, its theoretical underpinnings, and its solution algorithms, to provide a more streamlined, comprehensive, and useful ...
We consider a Markov chain model of influence in a network where inactive nodes can be activated by their active in-neighbors or by external factors.
We consider a Markov chain model of influence in a network where inactive nodes can be activated by their active in-neighbors or by external factors.
Krokhmal: A Scalable Markov Chain Framework for Influence Maximization in Arbitrary Networks. IEEE Trans. Netw. Sci. Eng. 8(3): 2372-2387 (2021). [+] ...
A scalable Markov chain framework for influence maximization in arbitrary networks. JS Borrero, M Akhgar, PA Krokhmal. IEEE Transactions on Network Science ...
a scalable solution for influence maximization under non- progressive LT model. ... Markov chain ... we perform influence maximization on networks with sizes.
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The influence maximization (IM) problem identifies the subset of influential users in the network to provide solutions for real-world problems.
Jan 1, 2025 · These novel methods aim for better generalization and scalability for more sizable graphs but face significant challenges, such as (1) inability ...
We consider a Markov chain model of influence in a network where inactive nodes can be activated by their active in-neighbors or by external factors ...