Adversarial graph embeddings for fair influence maximization over social networks

M Khajehnejad, AA Rezaei, M Babaei… - arXiv preprint arXiv …, 2020 - arxiv.org
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
被引用次数：52 相关文章
[PDF] monash.edu

[PDF][PDF] Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

M Khajehnejad, AA Rezaei, M Babaei, J Hoffmann… - researchmgt.monash.edu
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
[PDF] cam.ac.uk

[PDF][PDF] Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

M Khajehnejad, AA Rezaei, M Babaei, J Hoffmann… - mlg.eng.cam.ac.uk
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
[PDF] lcfi.ac.uk

[PDF][PDF] Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

M Khajehnejad, AA Rezaei, M Babaei, J Hoffmann… - lcfi.ac.uk
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
[PDF] academia.edu

[PDF][PDF] Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

M Khajehnejad, AA Rezaei, M Babaei, J Hoffmann… - academia.edu
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
[PDF] ijcai.org

[PDF][PDF] Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

M Khajehnejad, AA Rezaei, M Babaei, J Hoffmann… - ijcai.org
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …

Adversarial graph embeddings for fair influence maximization over social networks

M Khajehnejad, AA Rezaei, M Babaei… - Proceedings of the …, 2021 - dl.acm.org
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …

Adversarial graph embeddings for fair influence maximization over social networks

M Khajehnejad, AA Rezaei… - … of the Twenty …, 2021 - researchrepository.rmit.edu.au
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …

Adversarial Graph Embeddings for fair influence maximization over social networks

M Khajehnejad, AA Rezaei, M Babaei… - … Joint Conference on …, 2020 - research.monash.edu
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …
[PDF] researchgate.net

[PDF][PDF] Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

M Khajehnejad, AA Rezaei, M Babaei, J Hoffmann… - researchgate.net
Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …