A New Approach to Minimize Memory Requirements of Frequent Subgraph Mining Algorithms

Abstract

Frequent subgraph mining (FSM) is a subsection of graph mining domain which is extensively used for graph classification and graph clustering purposes. Over the past decade, many efficient FSM algorithms have been developed. The improvements generally focus on reducing time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms have another problem to solve, which is the high memory consumption. In this study, a new approach called Predictive Dynamic Sized Structure Packing (PDSSP) have been proposed to minimize the memory requirement of FSM algorithms. Proposed approach redesigns the internal data structures of FSM algorithms without any algorithmic modifications. PDSSP has two contributions. The first one is the Dynamic Sized Integer Type (ds_Int) which is a newly designed unsigned integer data type. The second contribution is “Data Structure packaging” component that uses a data structure packing technique which changes the behaviour of the compiler. A number of experiments have been conducted to examine the effectiveness and efficiency of the PDSSP approach by embedding it into two state-of-art algorithms called gSpan and Gaston. Proposed implementation have been compared to the official one. Almost all results show that the proposed implementation consumes less memory on each support level. As a result, PDSSP extensions can save memory and the peak memory usage may decrease up to 38% depending on the dataset.