Describe the types of modifications necessary to adapt the frequent subgraph mining algorithm to handle:

(a) Directed graphs
(b) Unlabeled graphs
(c) Acyclic graphs
(d) Disconnected graphs
For each type of graph given above, describe which step of the algorithm will
be affected (candidate generation, candidate pruning, and support counting),
and any further optimization that can help improve the efficiency of the
algorithm.

(a) Adjacency matrix may not be symmetric, which affects candidate gen-
eration using vertex growing approach.

(b) An unlabeled graph is equivalent to a labeled graph where all the ver-
tices have identical labels.

(c) No effect on algorithm. If the graph is a rooted labeled tree, more
efficient techniques can be developed to encode the tree (see: M.J. Zaki,
Efficiently Mining Frequent Trees in a Forest, In Proc. of the Eighth
ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining,
2002).