Humans need to navigate in complex networks almost daily: be it the street network of their hometown, harnessing the invisible network of social interaction to reach a certain goal, or solving a complex problem. But how do people learn how to navigate in a complex network which they do not know in total? We show some of the rules by which humans navigate complex networks in two examples: Together with our colleagues Sudarshan Iyengar, Abhiram Natarajan, and C. E. Veni Madhavan we have first explored this question by letting people play a simple word game, the wordMorph. It asks to find a way from a given starting word (e.g., DOG) to a final word (e.g., CAT) through a list of eligible English words by only exchanging a single letter in each step, as in DOG-DOT-COT-CAT. We can show that people learn to navigate the network much faster than expected and that they do so by picking certain ‘landmark’ words which they use as a guideline. We can show that these landmarks are the most central words in all paths they use, and that this time-efficient way of navigating in a complex network is also memory-efficient and almost as efficient as using the shortest possible paths. In a second cooperation with Marco Ragni and Mareike Bockholdt we have analyzed the way in which people solve a complex game by navigating from state to state. Again we show that people do not use shortest paths but are quite efficient in finding a near-optimal path. Future research will show how network analysis can help psychology to understand complex problem solving via understanding how humans navigate in complex networks.