Network analytical approaches have been applied to the study of young bilinguals’ word learning strategies by a growing body of research in various settings (for a review, see Wojcik, 2018). They have investigated the effect of one language on the structure of the other’s network or compared both networks, trying to identify similarities. These studies have identified features that influence bilinguals’ acquisition process: frequency, phonological features (Bilson et al., 2015), the presence of translation equivalents and cognates (Bosch & Ramon-Casas, 2014), and familiarity (Wojcik, 2018), among others.The present study was an exploratory attempt to capture the structure of the receptive vocabulary networks of young bilingual children, by combining both of their languages. Network analysis can model the connections or links of word-nodes based on selected lexical features. The links in the present social networks were determined by how many of the participating children knew two words at the same time. This knowledge was previously tested with the English (Dunn & Dunn, 1997) and Dutch (Schlichting, 2005) versions of the Peabody Picture Vocabulary Test (PPVT) with children with early exposure to both languages (Aalberse et al. [MIND-team], 2021). Exposure to Dutch and English at children's homes and daycare centers for each individual child was assessed through a parental and daycare questionnaire respectively (MIND-team, 2021). Social network analysis, using the ORA-lite software (Altman & Reminga, 2018), was used to create a network for all the children (N=297; MAge= 34.5 months, SDAge= 6.5 months) and two networks of two subgroups based on exposure (Group A: higher English than Dutch exposure, and Group B: higher Dutch than English exposure). Average exposure scores were used as a threshold and cut-off point to determine ‘high’ and ‘low’ exposure groups. The Leiden algorithm was applied to the networks (Traag et al., 2019). The algorithm is designed to identify groups, sets, and clusters within ...