Graph pattern matching is typically defined in terms of subgraph isomorphism, which makes
it an np-complete problem. Moreover, it requires bijective functions, which are often too …

Graph pattern matching is commonly used in a variety of emerging applications such as
social network analysis. These applications highlight the need for studying the following two …

Graph pattern matching is finding all matches in a data graph for a given pattern graph and
is often defined in terms of subgraph isomorphism, an np-complete problem. To lower its …

Graph pattern matching is often defined in terms of subgraph isomorphism, an NP-complete
problem. To lower its complexity, various extensions of graph simulation have been …

Graph pattern matching is fundamental to social network analysis. Traditional techniques
are subgraph isomorphism and graph simulation. However, these notions often impose too …

Graph pattern matching has been widely used in eg, social data analysis. A number of
matching algorithms have been developed that, given a graph pattern Q and a graph G …

In many graph--mining problems, two networks from different domains have to be matched.
In the absence of reliable node attributes, graph matching has to rely on only the link …

In a variety of emerging applications one needs to decide whether a graph G matches
another Gp, ie, whether G has a topological structure similar to that of Gp. The traditional …

Graph pattern matching, which aims to discover structural patterns in graphs, is considered
one of the most fundamental graph mining problems in many real applications. Despite …

Graph simulation has been adopted for pattern matching to reduce the complexity and
capture the need of novel applications. With the rapid development of the Web and social …