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

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

… 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 …

… 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 …

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

… 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 […

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

… In this article, we have modeled the influence maximization of complex networks as the
continuous … Cui, “DISCO: Influence maximization meets network embedding and deep learning,” …

… In this paper, inspired by deep learning techniques, we propose a novel approach to solve
the influence maximization problem as a classical regression task using transfer learning via …

… networks comprising millions of nodes. Secondly, even though deep node and network
embeddings, … Regrettably, these simplistic diffusion models fall short of meeting the demands of …