Graph based model for information retrieval using a stochastic local search

Graph has become increasingly important in modeling complicated structures and data such as chemical compounds, and social networks. Recent advance of machine learning research has witnessed a number of models based on graphs, from which information retrieval study is also benefited since many of these models have been verified by different information retrieval tasks. In this paper, we investigate the issues of indexing graphs and define a novel solution by applying a stochastic local search (SLS) method to extract subgraphs that will be used for the Information Retrieval process. To reduce the size of the index, we take into consideration the size of the query and the set of the frequent subgraphs. In other words, the subgraphs that will be used to create the index will have a size equal to the size of the query in order to optimize as much as possible the search space and the execution time. The proposed method is evaluated on the CACM collection, which contains the titles, authors and abstracts (where available) of a set of scientific articles and a set of queries and relevancy judgments and compared to the vector-based cosine model proposed in the literature. Our method is able to discover frequent subgraphs serving to establish the index and relevant documents for the Information Retrieval process's output. The numerical results show that our method provides competitive results and finds high quality solutions (documents) compared to the relevant documents cited on the CACM collection.