  Influence maximization (IM) is an important topic in network science where a
small seed set is chosen to maximize the spread of influence on a network.
Recently, this problem has attracted attention on temporal networks where the
network structure changes with time. IM on such dynamically varying networks is
the topic of this review. We first categorize methods into two main paradigms:
single and multiple seeding. In single seeding, nodes activate at the beginning
of the diffusion process, and most methods either efficiently estimate the
influence spread and select nodes with a greedy algorithm, or use a
node-ranking heuristic. Nodes activate at different time points in the multiple
seeding problem, via either sequential seeding, maintenance seeding or node
probing paradigms. Throughout this review, we give special attention to
deploying these algorithms in practice while also discussing existing solutions
for real-world applications. We conclude by sharing important future research
directions and challenges.
