Query-Sensitive Graph Partitioner for Pattern Matching Applications

A Scalable Distributed Graph Partitioner

We present Scalable Host-tree Embeddings for Efficient Partitioning (Sheep), a distributed graph partitioning algorithm capable of handling graphs that far exceed main memory. Sheep produces high quality edge partitions an order of magnitude faster than both state of the art offline (e.g., METIS) and streaming partitioners (e.g., Fennel). Sheep’s partitions are independent of the input graph di...

Generalized Graph Pattern Matching

Graphs are high expressive models and especially suitable for modelling highly complex structures, the number of applications that use them for modelling both their data and the processes that manipulate has grown dramatically in recent years. The use of new conceptual structures in real-world applications requires the development of new systems to store and query such structures in order to al...

Incremental Graph Pattern Matching

Graph matching applications in pattern recognition and image processing

In this paper we will try to characterize the role that graphs are conquering within the Pattern Recognition field. To this aim, a taxonomy built considering the most common applications of graph based techniques in the Pattern Recognition and Image Processing field is presented and discussed.

Rulegraphs for graph matching in pattern recognition

In Pattern Recognition, the Graph Matching problem involves the matching of a sample graph with the subgraph of a larger model graph where vertices and edges correspond to pattern parts and their relations. In this paper, we present Rulegraphs, a new method that combines the Graph Matching approach with Rule-Based approaches from Machine Learning. They reduce the cardinality of the (NP-Complete...