Deep graph representation learning and optimization for influence maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
Deep Graph Representation Learning and Optimization for Influence Maximization
C Ling, J Jiang, J Wang, M Thai, L Xue, J Song, M Qiu… - 2023 - openreview.net
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
Deep Graph Representation Learning and Optimization for Influence Maximization
C Ling, J Jiang, J Wang, M Thai, L Xue, J Song… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
Deep Graph Representation Learning and Optimization for Influence Maximization
MT Wang, L Zhao - 2023 - lingchen0331.github.io
Deep Graph Representation Learning and Optimization for Influence Maximization Page 1
Deep Graph Representation Learning and Optimization for Influence Maximization Joint work …
Deep Graph Representation Learning and Optimization for Influence Maximization Joint work …
Deep graph representation learning and optimization for influence maximization
C Ling, J Jiang, J Wang, MT Thai, L Xue… - Proceedings of the 40th …, 2023 - dl.acm.org
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
[PDF][PDF] Deep Graph Representation Learning and Optimization for Influence Maximization
C Ling, J Jiang, J Wang, M Thai, L Xue, J Song, M Qiu… - researchgate.net
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
Deep Graph Representation Learning and Optimization for Influence Maximization
C Ling, J Jiang, J Wang, M Thai, L Xue, J Song… - arXiv preprint arXiv …, 2023 - arxiv.org
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
[PDF][PDF] Deep Graph Representation Learning and Optimization for Influence Maximization
C Ling, J Jiang, J Wang, MT Thai, L Xue, J Song, M Qiu… - proceedings.mlr.press
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
[PDF][PDF] Deep Graph Representation Learning and Optimization for Influence Maximization
C Ling, J Jiang, J Wang, MT Thai, L Xue, J Song, M Qiu… - ICML, 2023 - par.nsf.gov
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …