Abstract Graph is a prevalent data structure that enables many predictive tasks. How to engineer graph features is a fundamental question. Our concept is to go beyond nodes and edges, and explore richer structures (e.g., paths, subgraphs) for graph feature engineering. We call such richer structures as network functional blocks, because each structure serves as a network building block but with some different functionality. We use semantic proximity search as an example application to share our recent work on exploiting different granularities of network functional blocks. We show that network functional blocks are effective, and they can be useful for a wide range of applications.