There exist many graph-based applications including bioinformatics, social science, link analysis, citation analysis, and collaborative work. All need to deal with a large data graph. Given a large data graph, in this paper, we study finding top-A; answers for a graph query, and in particular, we focus on top-A; cyclic graph queries where a graph query is cyclic and can be complex. The capability of supporting top-k cyclic graph queries over a data graph provides much more flexibility for a user to search graphs. And the problem itself is challenging. After investigating a direct yet infeasible solution, we propose a new twig query approach. In our approach, we first identify a spanning tree of the cyclic graph query, which is used to generate a list of ranked twig answers on-demand. Then we identify the top-A; answers for the graph query based on the twig answer list. In order to find the best twig query in solving a given cyclic graph query, cost-based optimization for twig query selection is studied. We conducted extensive performance studies using a real dataset, and we report our findings in this paper.
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