LinkedIn’s professional network exhibits a direct network effect with an unusual property: the value is not symmetric. Adding a well-connected executive creates more value than adding a recently graduated student, because the executive’s connections open more professional paths. This asymmetry means LinkedIn’s growth strategy focused on recruiting senior professionals early, so that the network was valuable to junior professionals when they joined. The architecture had to handle a heterogeneous graph where node value varied by many orders of magnitude.
Uber exhibits a cross-side network effect. More drivers mean shorter wait times for riders. More riders mean shorter idle times for drivers. The matching algorithm is the core architectural mechanism. Its quality determines the platform’s liquidity — the probability that a rider finds a driver quickly, and vice versa. At low density, both sides experience the cold-start problem simultaneously: drivers idle because there are few riders; riders wait because there are few drivers. Uber’s geographic focus strategy addressed this by achieving density in small areas before expanding.
Google Search exhibits a data network effect. More queries reveal which search results users find useful and which they do not. The click-through and dwell-time signals from billions of queries are training data for the ranking model. A new entrant with a technically superior ranking algorithm but fewer user signals will produce worse results than Google, because the ranking model trained on Google’s data is more accurate. The data advantage compounds over time.