This article introduces two quantitative indices, \( d' \) and \( H_2' \), to measure the degree of specialization in species interaction networks. These indices are based on interaction frequencies and are derived from Shannon entropy. The species-level index \( d' \) describes the degree of interaction specialization at the species level, while the network-level index \( H_2' \) characterizes the degree of specialization or partitioning among two parties in the entire network. Both indices are mathematically related and can be used to analyze variation within networks and for comparisons across different interaction webs. The authors demonstrate that these quantitative measures are more robust against variation in sampling intensity, network size, and symmetry compared to qualitative indices such as connectance or the number of links. They apply these indices to two published pollinator networks and find that they reveal differences and features not detected by previous approaches, such as differences in average degree of specialization and the correlation between specialization and relative abundance. The study suggests that these new indices will improve our understanding of patterns of specialization within and across networks from a broad spectrum of biological interactions.This article introduces two quantitative indices, \( d' \) and \( H_2' \), to measure the degree of specialization in species interaction networks. These indices are based on interaction frequencies and are derived from Shannon entropy. The species-level index \( d' \) describes the degree of interaction specialization at the species level, while the network-level index \( H_2' \) characterizes the degree of specialization or partitioning among two parties in the entire network. Both indices are mathematically related and can be used to analyze variation within networks and for comparisons across different interaction webs. The authors demonstrate that these quantitative measures are more robust against variation in sampling intensity, network size, and symmetry compared to qualitative indices such as connectance or the number of links. They apply these indices to two published pollinator networks and find that they reveal differences and features not detected by previous approaches, such as differences in average degree of specialization and the correlation between specialization and relative abundance. The study suggests that these new indices will improve our understanding of patterns of specialization within and across networks from a broad spectrum of biological interactions.