Interconnectedness is an alternative risk concept that has so far received little attention in academia and the asset management industry. We show that this neglect is unjustified, as interconnectedness risk has only moderate or no connection to conventional portfolio optimization inputs, and active investment strategies based on interconnectedness information outperform their conventional peers. Utilizing a multi-asset data set, we measure interconnectedness risk by the embeddedness intensity, ie, centrality, of assets in a correlation network, a concept from graph theory. Using the most common centrality measures, we first conduct empirical similarity studies to analyze how different centrality scores relate to each other and to conventional portfolio optimization inputs. Then we outline how centrality can be incorporated into risk-based and risk–return-based frameworks. Out-of-sample performance studies of centrality optimized portfolios prove their competitiveness.