A path P v, w) is an ordered set of nodes v = v0, v1,..., v k = w such that for any two subsequent nodes in that order (v i, v i +1) is an edge in E. The length of a path in an unweighted graph is given by the number of edges in it.
A minimum cycle basis is a cycle basis of minimum weight, in an unweighted graph the weight is simply the number of edges.
To perform classification process, an unweighted graph H= V, E) is firstly built, where V is a set of nodes modeling the set R of metagenomic reads, and E is a set of edges.
In an unweighted graph any spanning tree is the minimum spanning tree.
MCL is based on flow simulation in the graph and in an unweighted graph the clustering results depend only on its topology.
Croes et al. compared this weighted graph search with an unweighted graph and a filtered graph (where 36 highly connected pool metabolites were removed), and found that weighted graph search performed better (85% correspondence with annotated pathways) than the unweighted graph search (30%) and filtered graph search (65%).
In an unweighted graph, the probability that the walker moves to each connected vertex is uniform.
In addition, note that for an unweighted graph, the threshold function definition of a tight set S becomes (f_{k} (S) = { v in V mid | N v) cap S |ge k }), that is (operatorname{cl}(S)) generates a k-core.
The final score was an unweighted sum of each subscale.
