Table 1.

STM algorithm

Algorithm 1: STM(G)
1: V: set of nodes in Graph G
2: F(c): Transduction behavior function
3: S(v, w): arrived signal from node v to node w
4: C: the list of final clusters
5: PreClusters: the list of preliminary clusters
6: for each node pair(v, w) v, w ∈ V, v ≠ w do
7:    distance(v, w) ← the shortest path length from node v to node w
8:    set parameter c in function as F(c) as distance(v, w)
9:    signal(v, w) ← $S(v\to w)=\frac{d(v)}{{\displaystyle {\prod }_{i\in P(v,w)}d(i)}}F(c)$
10: end for
11: for each node v ∈ V do
12:       v. representative ← select the best scored node w for node v
13:       if cluster_w == null then
14:          make cluster_w
15:          cluster_w.add(v)
16:          PreClusters.add(cluster_w)
17:       else
18:          cluster_w.add(v)
19:       end if
20: end for
21: C ← Merge(PreClusters)