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The case of exact graph matching is known as the graph isomorphism problem. [1] The problem of exact matching of a graph to a part of another graph is called subgraph isomorphism problem . Inexact graph matching refers to matching problems when exact matching is impossible, e.g., when the number of vertices in the two graphs are different.
This problem is a special case of the subgraph isomorphism problem, [5] which asks whether a given graph G contains a subgraph that is isomorphic to another given graph H; this problem is known to be NP-complete. It is also known to be a special case of the non-abelian hidden subgroup problem over the symmetric group. [6]
A matching M of a graph G is maximal if every edge in G has a non-empty intersection with at least one edge in M. The following figure shows examples of maximal matchings (red) in three graphs. A maximum matching (also known as maximum-cardinality matching [2]) is a matching that contains the largest possible number of edges. There may be many ...
The next example is about regular graphs. WLtest cannot distinguish regular graphs of equal order, [4]: 31 but WLpair can distinguish regular graphs of distinct degree even if they have the same order. In fact WLtest terminates after a single round as seen in these examples of order 8, which are all 3-regular except the last one which is 5-regular.
A call graph generated for a simple computer program in Python. A call graph (also known as a call multigraph [1] [2]) is a control-flow graph, [3] which represents calling relationships between subroutines in a computer program.
Matching (graph theory) – matching between different vertices of the graph; usually unrelated to preference-ordering. Envy-free matching – a relaxation of stable matching for many-to-one matching problems; Rainbow matching for edge colored graphs; Stable matching polytope; Lattice of stable matchings
In the mathematical discipline of graph theory, the line graph of an undirected graph G is another graph L(G) that represents the adjacencies between edges of G. L(G) is constructed in the following way: for each edge in G, make a vertex in L(G); for every two edges in G that have a vertex in common, make an edge between their corresponding vertices in L(G).
Finally, it locates an augmenting path P′ in the contracted graph (line B22) and lifts it to the original graph (line B23). Note that the ability of the algorithm to contract blossoms is crucial here; the algorithm cannot find P in the original graph directly because only out-of-forest edges between vertices at even distances from the roots ...