Scalable Diversified Top-k Pattern Matching in Big Graphs

Abstract

Typically, graph pattern matching is expressed in terms of subgraph isomorphism. Graph simulation and its variants were introduced to reduce the time complexity and obtain more meaningful results in big graphs. Among these models, the matching subgraphs obtained by tight simulation are more compact and topologically closer to the pattern graph than results produced by other approaches. However, the number of resulting subgraphs can be huge, overlapping each other and unequally relaxed from the pattern graph. Hence, we introduce a ranking and diversification method for tight simulation results, which allows the user to obtain the most diversified and relevant matching subgraphs. This approach exploits the weights on edges of the big graph to express the interest of the matching subgraph by tight simulation. Furthermore, we provide distributed scalable algorithms to evaluate the proposed methods based on distributed programming paradigms. The experiments on real data graphs succeed in demonstrating the effectiveness of the proposed models and the efficiency of the associated algorithms. The result diversification reached 123% within a time frame that does not exceed 40%, on average, of the duration required for tight simulation graph pattern matching.