  The inclusion of link weights into the analysis of network properties allows
a deeper insight into the (often overlapping) modular structure of real-world
webs. We introduce a clustering algorithm (CPMw, Clique Percolation Method with
weights) for weighted networks based on the concept of percolating k-cliques
with high enough intensity. The algorithm allows overlaps between the modules.
First, we give detailed analytical and numerical results about the critical
point of weighted k-clique percolation on (weighted) Erdos-Renyi graphs. Then,
for a scientist collaboration web and a stock correlation graph we compute
three-link weight correlations and with the CPMw the weighted modules. After
reshuffling link weights in both networks and computing the same quantities for
the randomised control graphs as well, we show that groups of 3 or more strong
links prefer to cluster together in both original graphs.
