The paper introduces the concepts of *intensity* and *coherence* for subgraphs in weighted complex networks, generalizing the traditional approach used for unweighted networks. The *intensity* of a subgraph is defined as the geometric mean of its link weights, while the *coherence* is the ratio of the geometric to the arithmetic mean of the weights. These measures are used to define *motif intensity scores* and a *weighted clustering coefficient*. The authors demonstrate these concepts by applying them to financial and metabolic networks. In the financial network, the weighted clustering coefficient captures the effects of a market crash more effectively than other clustering characteristics. In the metabolic network, the inclusion of weights significantly modifies the conclusions drawn from motif statistics. The results highlight the importance of considering interaction strengths in network analysis.The paper introduces the concepts of *intensity* and *coherence* for subgraphs in weighted complex networks, generalizing the traditional approach used for unweighted networks. The *intensity* of a subgraph is defined as the geometric mean of its link weights, while the *coherence* is the ratio of the geometric to the arithmetic mean of the weights. These measures are used to define *motif intensity scores* and a *weighted clustering coefficient*. The authors demonstrate these concepts by applying them to financial and metabolic networks. In the financial network, the weighted clustering coefficient captures the effects of a market crash more effectively than other clustering characteristics. In the metabolic network, the inclusion of weights significantly modifies the conclusions drawn from motif statistics. The results highlight the importance of considering interaction strengths in network analysis.