Frequent Subgraph Mining is an essential operation for graph analytics and knowledge
extraction. Due to its high computational cost, parallel solutions are necessary. Existing …

The Rainbow Range Finder (RRF) has the ability to obtain range information for all image
pixels with only one frame TV camera imaging during 1/30 s. The authors propose a novel …

We present MaNIACS, a sampling-based randomized algorithm for computing high-quality
approximations of the collection of the subgraph patterns that are frequent in a single, large …

Mining frequent subgraphs is an interesting and important problem in the graph mining field,
in that mining frequent subgraphs from a single large graph has been strongly developed …

Mining frequent subgraphs is an important issue in graph mining. It is defined as finding all
subgraphs whose occurrences in the dataset are greater than or equal to a given frequency …

Mining a weighted single large graph has recently attracted many researchers. The
WeGraMi algorithm is considered the state-of-the-art among current approaches. It uses a …

Frequent subgraph mining (FSM) has important applications in areas such as bioinformatics,
social networks and others. In this paper, we present a highly scalable approach called …

In many recent applications, a graph is used to simulate many complex systems, such as
social networks, traffic models or bioinformatics, and the underlying graphs for these …

In modern applications, large graphs are usually applied in the simulation and analysis of
large complex systems such as social networks, computer networks, maps, traffic networks …

Data Mining has a subpart called Frequent Subgraph Mining (FSM) and is a demanding
area for the implementation of graph classification and graph clustering which is used in the …