Texture analysis has attracted increasing attention in computer vision due to its power in describing images and the physical properties of objects. Among the methods for texture analysis, complex network (CN)-based ones have emerged to model images because of their flexibility. In image modeling, each pixel is mapped to a vertex of the CN and two vertices are connected if they are spatially close in the image. Then measurements are extracted from the CN to characterize its topology and therefore characterize the image content. Despite the promising results, the accuracy of these methods depends on the suitability of the measurement for the application. In texture analysis, simple measurements have been used, such as those based on vertex degree and shortest paths. Motivated by these issues, this paper proposes a new method for texture analysis based on the CN and a new measurement that calculates the importance of each vertex within its neighborhood. For calculating the importance of vertices, we extend the pagerank to CN in order to correlate the vertex importance with its degree and show that this new measurement extracts texture properties. Experimental results on well-known datasets and in the recognition of soybean diseases using leaf texture show the effectiveness of the proposed method for texture recognition.