symbolic struggles have a central place in Bourdieu’s field theory, especially in the theory of cultural production fields such as literary. Bourdieu conceptualises fields and associated battles with terms of position space (in capital distributions) and position space, which form, inter alia, mutual classifications and evaluations of field actors. Therefore, a literary field can be conceptualised and operationalised as a network of peer-to-peer classifications and evaluations, where the co-created or predicator(s) of these interactions are capital as attributes of the author. Analysing the binary network of cross-referencing among Dutch literary authors in 1976 (de Nooy, 1991; de Nooy and Others, 2005), in the light of the classifications in literary routes given to them ‘outside’, I shall endeavour to demonstrate that the technique of the exponential random graph model (ERGM) is adequate for such operationalisation of the literary field and symbolic struggles. ERGMs recognise local and global configurations in the network when examining the relationship between actor’s attributes and interactions. For example, if the empirical network is characterised by patterns such as balance and transit, it is necessary to take them into account in order to be able to conclude on the attributes of the authors as co-creators of their interactions with each other. Thus, as I shall try to demonstrate in representations, analysis and debate, unlike the exploratory and inferential techniques of social media analysis previously applied to literary fields and literary criticism (de Nooy, 1991, 1999, 2008; Anheier et al. 1995), ERGMs enable or require concurrent modelling of the position space and the space for taking positions in the field. The added value of such operationalisation is the relative indicators of symbolic capital in the literary field: the prominence of the author is expressed through network centrality measures, such as input grades, and through intermediary roles (Gould and Fernandez, 1989). Social capital, operationalised by socio-centric networks, can be combined in the ERGM with multiple interaction networks, which could form subdomains of peer-to-peer classifications and multiplex networks.