Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Discriminative subgraphs can be used to characterize complex graphs, construct graph classifiers and generate graph indices. The search space for discriminative subgraphs is usually prohibitively large. Most measurements of interestingness of discriminative subgraphs are neither monotonic nor antimonotonic with respect to subgraph frequencies. Therefore, branch-and-bound algorithms are unable to mine discriminative subgraphs efficiently.  discover that search history of discriminative subgraph mining is very useful in computing empirical upper-bounds of discrimination scores of subgraphs.  propose a novel discriminative subgraph mining method, LTS (Learning To Search), which begins with a greedy algorithm that first samples the search space through subgraph probing and then explores the search space in a branch and bound fashion leveraging the search history of these samples. Extensive experiments have been performed to analyze the gain in performance by taking into account search history and to demonstrate that LTS can significantly improve performance compared with the state-of-the-art discriminative subgraph mining algorithms.
['Ning Jin', 'Wei Wang']
I am writing a research paper on a idea LTS: Discriminative subgraph mining by learning from search history
[{"from": "human", "value": "I am writing a research paper on a idea LTS: Discriminative subgraph mining by learning from search history"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Discriminative subgraphs can be used to characterize complex graphs, construct graph classifiers and generate graph indices. The search space for discriminative subgraphs is usually prohibitively large. Most measurements of interestingness of discriminative subgraphs are neither monotonic nor antimonotonic with respect to subgraph frequencies. Therefore, branch-and-bound algorithms are unable to mine discriminative subgraphs efficiently.  discover that search history of discriminative subgraph mining is very useful in computing empirical upper-bounds of discrimination scores of subgraphs.  propose a novel discriminative subgraph mining method, LTS (Learning To Search), which begins with a greedy algorithm that first samples the search space through subgraph probing and then explores the search space in a branch and bound fashion leveraging the search history of these samples. Extensive experiments have been performed to analyze the gain in performance by taking into account search history and to demonstrate that LTS can significantly improve performance compared with the state-of-the-art discriminative subgraph mining algorithms."}]