We address the problem of top-k search on graphs with multiple nodal attributes, which we call WAGs (short for Weighted Attribute Graphs). For example, a co-authorship network is a WAG, where each author is a node; each attribute corresponds to a particular topic (e.g., databases, data mining, and machine learning); and the amount of expertise in a particular topic is represented by a non-negative weight on that attribute. A typical search in this setting may be: find three coauthors (i.e., a triangle) where each author's expertise is greater than 50% in at least one topic area (i.e., attribute). We show that the problem of retrieving the optimal answer for graph search on WAGs is NP-complete. Moreover, we propose a fast and effective top-k graph search algorithm for WAGs. In an extensive experimental study, our proposed algorithm exhibits significant speed-up over competing approaches. On average, our proposed method achieves 7× faster query processing than the best competitor.