O(n2.5) time algorithms for the subgraph homeomorphism problem on trees
Journal of Algorithms
On the complexity of finding iso- and other morphisms for partial k-trees
Discrete Mathematics - Topological, algebraical and combinatorial structures; Froli´k's memorial volume
The parallel complexity of tree embedding problems
Journal of Algorithms
Ordered and Unordered Tree Inclusion
SIAM Journal on Computing
On the agreement of many trees
Information Processing Letters
Maximum Agreement Subtree in a Set of Evolutionary Trees: Metrics and Efficient Algorithms
SIAM Journal on Computing
Tree compatibility and inferring evolutionary history
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An O(n log n) algorithm for the maximum agreement subtree problem for binary trees
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Fast comparison of evolutionary trees
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Shock Graphs and Shape Matching
International Journal of Computer Vision
ICALP '94 Proceedings of the 21st International Colloquium on Automata, Languages and Programming
Alignment of Trees - An Alternative to Tree Edit
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Graph Theory With Applications
Graph Theory With Applications
Efficient tree pattern matching
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Hiding the policy in cryptographic access control
STM'11 Proceedings of the 7th international conference on Security and Trust Management
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The diversity of application areas relying on tree-structured data results in a wide interest in algorithms which determine differences or similarities among trees. One way of measuring the similarity between trees is to find the smallest common superstructure or supertree, where common elements are typically defined in terms of a mapping or embedding. In the simplest case, a supertree will contain exact copies of each input tree, so that for each input tree, each vertex of a tree can be mapped to a vertex in the supertree such that each edge maps to the corresponding edge. More general mappings allow for the extraction of more subtle common elements captured by looser definitions of similarity. We consider supertrees under the general mapping of minor containment. Minor containment generalizes both subgraph isomorphism and topological embedding; as a consequence of this generality, however, it is NP-complete to determine whether or not G is a minor of H, even for general trees. By focusing on trees of bounded degree, we obtain an O(n3) algorithm which determines the smallest tree T such that both of the input trees are minors of T, even when the trees are assumed to be unrooted and unordered.