Sufficient Networks for Computing Support of Graph Patterns.

Graph mining is the process of extracting and analyzing patterns from graph data. Graphs are a data structure that consists of a set of nodes and a set of edges that connect these nodes. Graphs are often used to represent real-world entities and the relationships between them. In a graph database, the importance of a pattern (also known as support) must be quantified using a counting function called a support measure. This function must adhere to several constraints, such as antimonotonicity that forbids a pattern to have support bigger than its sub-patterns. These constraints make the tasks of defining and computing support measures highly non-trivial and computationally expensive. In this paper, I use the previously discovered relationship between support measures in graph databases and flows in networks of subgraph appearances to simplify the process of computing support measures. I show that the network of pattern instances may be successfully pruned to contain just particular kinds of patterns and prove that any legitimate computing support measures in graph databases can adopt this strategy. When the suggested method is utilized, experimental evaluation demonstrates that network size reduction is significant. [ABSTRACT FROM AUTHOR]