Download presentation

Presentation is loading. Please wait.

Published byReid Paxson Modified over 2 years ago

1
Graph Isomorphism Algorithms and networks

2
Graph Isomorphism 2 Today Graph isomorphism: definition Complexity: isomorphism completeness The refinement heuristic Isomorphism for trees –Rooted trees –Unrooted trees

3
Graph Isomorphism 3 Two graphs G=(V,E) and H=(W,F) are isomorphic if there is a bijective function f: V W such that for all v, w V: –{v,w} E {f(v),f(w)} F

4
Applications Chemistry: databases of molecules (etc.) –Actually needed: canonical form of molecule structure / graph Other, e.g., speeding up algorithms for highly symmetric graphs Graph Isomorphism 4

5
5 Variant for labeled graphs Let G = (V,E), H=(W,F) be graphs with vertex labelings l: V L, l’ L. G and H are isomorphic labeled graphs, if there is a bijective function f: V W such that –For all v, w V: {v,w} E {f(v),f(w)} F –For all v V: l(v) = l’(f(v)). Application: organic chemistry: –determining if two molecules are identical.

6
Graph Isomorphism 6 Complexity of graph isomorphism Problem is in NP, but –No NP-completeness proof is known –No polynomial time algorithm is known If GI is NP-complete, then “strange things happen” –“Polynomial time hierarchy collapses to a finite level” NP NP-complete P Graph isomorphism If P NP ?

7
Graph Isomorphism 7 Isomorphism-complete Problems are isomorphism- complete, if they are `equally hard’ as graph isomorphism –Isomorphism of bipartite graphs –Isomorphism of labeled graphs –Automorphism of graphs Given: a graph G=(V,E) Question: is there a non-trivial automorphism, i.e., a bijective function f: V V, not the identity, with for all v,w V: –{v,w} E, if and only if {f(v),f(w)} E.

8
Graph Isomorphism 8 More isomorphism complete problems Finding a graph isomorphism f Isomorphism of semi-groups Isomorphism of finite automata Isomorphism of finite algebra’s Isomorphism of –Connected graphs –Directed graphs –Regular graphs –Perfect graphs –Chordal graphs –Graphs that are isomorphic with their complement

9
Graph Isomorphism 9 Special cases are easier Polynomial time algorithms for –Graphs of bounded degree –Planar graphs –Trees –Bounded treewidth Expected polynomial time for random graphs This course

10
Graph Isomorphism 10 An equivalence relation on vertices Say v ~ w, if and only if there is an automorphism mapping v to w. ~ is an equivalence relation Partitions the vertices in automorphism classes Tells on structure of graph

11
Graph Isomorphism 11 Iterative vertex partition heuristic the idea Partition the vertices of G and H in classes Each class for G has a corresponding class for H. Property: vertices in class must be mapped to vertices in corresponding class Refine classes as long as possible When no refinement possible, check all possible ways that `remain’.

12
Graph Isomorphism 12 Iterative vertex partition heuristic If |V| |W|, or |E| |F|, output: no. Done. Otherwise, we partition the vertices of G and H into classes, such that –Each class for G has a corresponding class for H –If f is an isomorphism from G to H, then f(v) belongs to the class, corresponding to the class of v. First try: vertices belong to the same class, when they have the same degree. –If f is an isomorphism, then the degree of f(v) equals the degree of v for each vertex v.

13
Graph Isomorphism 13 Scheme Start with sequence SG = (A 1 ) of subsets of G with A 1 =V, and sequence SH = (B 1 ) of subsets of H with B 1 =W. Repeat until … –Replace A i in SG by A i1,…,A ir and replace B i in SH by B i1,…,B ir. A i1,…,A ir is partition of A i B i1,…,B ir is partition of B i For each isormorphism f: v in A ij if and only if f(v) in B ij

14
Graph Isomorphism 14 Possible refinement Count for each vertex in A i and B i how many neighbors they have in A j and B j for some i, j. Set A is = {v in A i | v has s neighbors in A j }. Set B is = {v in B i | v has s neighbors in B j }. Invariant: for all v in the ith set in SG, f(v) in the ith set in SH. If some |A i | |B i |, then stop: no isomorphism.

15
Graph Isomorphism 15 Other refinements Partition upon other characteristics of vertices –Label –Number of vertices at distance d (in a set A i ). –…

16
Graph Isomorphism 16 After refining If each A i contains one vertex: check the only possible isomorphism. Otherwise, cleverly enumerate all functions that are still possible, and check these. Works well in practice!

17
Graph Isomorphism 17 Isomorphism on trees Linear time algorithm to test if two (labeled) trees are isomorphic. (Aho, Hopcroft, Ullman, 1974) Algorithm to test if two rooted trees are isomorphic. Used as a subroutine for unrooted trees.

18
Graph Isomorphism 18 Rooted tree isomorphism For a vertex v in T, let T(v) be the subtree of T with v as root. Level of vertex: distance to root. If T 1 and T 2 have different number of levels: not isomorphic, and we stop. Otherwise, we continue:

19
Graph Isomorphism 19 Structure of algorithm Tree is processed level by level, from bottom to root Processing a level: –A long label for each vertex is computed –This is transformed to a short label Vertices in the same layer whose subtrees are isomorphic get the same labels: –If v and w on the same level, then L(v)=L(w), if and only if T(v) and T(w) are isomorphic with an isomorphism that maps v to w.

20
Graph Isomorphism 20 Labeling procedure For each vertex, get the set of labels assigned to its children. Sort these sets. –Bucketsort the pairs (L(w), v) for T 1, w child of v –Bucketsort the pairs (L(w), v) for T 2, w child of v For each v, we now have a long label LL(v) which is the sorted set of labels of the children. Use bucketsort to sort the vertices in T 1 and T 2 such that vertices with same long label are consecutive in ordering.

21
Graph Isomorphism 21 On sorting w.r.t. the long lists (1) Preliminary work: –Sort the nodes is the layer on the number of children they have. Linear time. (Counting sort / Radix sort.) –Make a set of pairs (j,i) with (j,i) in the set when the jth number in a long list is i. –Radix sort this set of pairs.

22
Graph Isomorphism 22 On sorting w.r.t. the long lists (2) Let q be the maximum length of a long list Repeat –Distribute among buckets the nodes with at least q children, with respect to the qth label in their long list Nodes distributed in buckets in earlier round are taken here in the order as they appear in these buckets. The sorted list of pairs (j,i) is used to skip empty buckets in this step. –q --; –Until q=0.

23
Graph Isomorphism 23 After vertices are sorted with respect to long label Give vertices with same long label same short label (start counting at 0), and repeat at next level. If we see that the set of labels for a level of T 1 is not equal to the set for the same level of T 2, stop: not isomorphic.

24
Graph Isomorphism 24 Time One layer with n 1 nodes with n 2 nodes in next layer costs O(n 1 + n 2 ) time. Total time: O(n).

25
Graph Isomorphism 25 Unrooted trees Center of a tree –A vertex v with the property that the maximum distance to any other vertex in T is as small as possible. –Each tree has a center of one or two vertices. Finding the center: –Repeat until we have a vertex or a single edge: Remove all leaves from T. –O(n) time: each vertex maintains current degree in variable. Variables are updated when vertices are removed, and vertices put in set of leaves when their degree becomes 1.

26
Graph Isomorphism 26 Isomorphism of unrooted trees Note: the center must be mapped to the center If T 1 and T 2 both have a center of size 1: –Use those vertices as root. If T 1 and T 2 both have a center of size 2: –Try the two different ways of mapping the centers –Or: subdivide the edge between the two centers and take the new vertices as root Otherwise: not isomorphic. 1 or 2 calls to isomorphism of rooted trees: O(n).

27
Graph Isomorphism 27 Conclusions Similar methods work for finding automorphisms We saw: heuristic for arbitrary graphs, algorithm for trees There are algorithms for several special graph classes (e.g., planar graphs, graphs of bounded degree,…)

Similar presentations

OK

Theory of Computational Complexity Probability and Computing Chapter Hikaru Inada Iwama and Ito lab M1.

Theory of Computational Complexity Probability and Computing Chapter Hikaru Inada Iwama and Ito lab M1.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on diode transistor logic Endocrine system anatomy and physiology ppt on cells Ppt on places in our neighbourhood living Ppt on pollution in india free download Ppt on good manners for kindergarten Run ppt on raspberry pi Ppt on science fiction in films Ppt on db2 introduction to economics Ppt on delhi election 2013 Ppt on media revolution entertainment