  Aiming at selecting a small subset of nodes with maximum influence on
networks, the Influence Maximization (IM) problem has been extensively studied.
Since it is #P-hard to compute the influence spread given a seed set, the
state-of-the-art methods, including heuristic and approximation algorithms,
faced with great difficulties such as theoretical guarantee, time efficiency,
generalization, etc. This makes it unable to adapt to large-scale networks and
more complex applications. On the other side, with the latest achievements of
Deep Reinforcement Learning (DRL) in artificial intelligence and other fields,
lots of works have been focused on exploiting DRL to solve combinatorial
optimization problems. Inspired by this, we propose a novel end-to-end DRL
framework, ToupleGDD, to address the IM problem in this paper, which
incorporates three coupled graph neural networks for network embedding and
double deep Q-networks for parameters learning. Previous efforts to solve IM
problem with DRL trained their models on subgraphs of the whole network, and
then tested on the whole graph, which makes the performance of their models
unstable among different networks. However, our model is trained on several
small randomly generated graphs with a small budget, and tested on completely
different networks under various large budgets, which can obtain results very
close to IMM and better results than OPIM-C on several datasets, and shows
strong generalization ability. Finally, we conduct a large number of
experiments on synthetic and realistic datasets, and experimental results prove
the effectiveness and superiority of our model.
