Maximal cliques in a multipartite graph - efficient? I am looking at a combinatorial optimisation problem where I have N classes and k objects of each class.
Now I am looking for the optimal subset such that each of the N classes is represented exactly once. Not all objects can be combined with each other, e.g. object (N=1,k=1) might not be compatible with (N=2, k=4).
I can formulate this problem as looking for the maximal clique on a N-partite graph (but maybe there are other formulations that I'm not aware of). My question is whether an efficient algorithm exists for solving this problem.
From https://stackoverflow.com/questions/2801138/find-all-complete-sub-graphs-within-a-graph:

This is known as the clique problem; it's hard and is in NP-complete in general, and yes there are many algorithms to do this.
If the graph has additional properties (e.g. it's bipartite), then the problem becomes considerably easier and is solvable in polynomial time

This implies that an efficient algorithm might exist for my problem, but I can't find it...
A maximal clique and a maximum clique are in general different. A set of vertices $S$ is a maximal clique if $S$ is a clique and you cannot add any vertex to $S$ such that the resulting set would form a clique. The set $S$ forms a maximum clique when there is no other set of vertices that forms a clique that is larger than $S$.

You mention bipartite graphs, but the complete $k$-partite graph is not bipartite if $k \geq 3$ (but it is for $k=2$). A bipartite graph contains no triangles, so in such a graph a maximum clique has size (at most) 2, so an efficient algorithm can just return a single edge.

Let $G = K_{n_1,\ldots,n_q}$ for positive integers $n_1,n_2,\ldots,n_q \geq 2$. You can verify that $G$ has maximum clique size $q$, and a total of $n_1 \cdot n_2 \cdots \cdot n_q$ such cliques. An algorithm for enumerating all of them reduces to basic combinatorics (pick one vertex, then how many choices for the remaining vertices do you have when forming a clique?)