Abstract: In a social attributed graph nodes have attributes labeled with a type of work title. So, every node is considered as object having its own work title as its attribute. The researchers detect user query pattern as sub-graphs in the social attributed graph using graph mining techniques. In this scenario the researchers propose university graph as a social attributed graph which compromises of various nodes having its attributes labeled with a type of work title such as Dean, Associate Dean, Professor and Associate Professor. Using the above listed attributes and the actual number of nodes, the authors design the proposed model as undirected graph. From this attributed graph, the authors detect line and loop pattern (or query). For this the authors propose an algorithm to detect line and loop pattern from the university attributed graph using graph mining techniques.