… 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 great …

… embedding and graph neural networks. This study intends to convert the problem of influence
maximization in complex networks … network embedding and deep reinforcement learning …

… learns network embeddings purely from linkage of the network… the embedding and influences
quality into a unified learning … , “DISCO: Influence maximization meets network embedding …

… meet the demand for algorithm performance due to the growing … Particularly, in the network
embedding learning, structures for … of DISCO that incorporates network embedding and DRL […

… deep reinforcement learning to solve influence maximization problem in signed social … Cui,
“Disco: Influence maximization meets network embedding and deep learning,” arXiv preprint …

… • We propose a DRL-based framework leveraging network embedding to address the CIM
… Cui, “Disco: Influence maximization meets network embedding and deep learning,” arXiv …

… in influence maximization and as we will see in the experimental section, its use of the
powers of influence … Finally, recent work on learning contingency-aware IM (Chen et al. 2021) …

… 1) Ablation Study: In the early version (called DISCO [39… a seed and recompute the network
embeddings as well as the Q … , “Influence maximization: Near-optimal time complexity meets …

… communities present in the network using network embedding backed model by extracting
… Therefore, there is a two-level path to meet the requirement of obtaining influential seed set …

… novel deep learning algorithm for influence maximization on heterogeneous networks based
… -IM, a heterogeneous network embedding method for identifying the influential seed set in …