Influence maximisation is the combinatorial optimisation problem of selecting a small set of seed nodes in a network so as to maximise the expected spread of information, adoption or behaviour under a diffusion model such as independent cascade or linear threshold. It is NP-hard in general, so practical algorithms rely on submodularity-based greedy approximation or scalable heuristics. It is applied in social network analysis for viral marketing, epidemic containment planning and identifying key influencers.