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As digital systems increasingly mediate advertising, the field must move beyond message effects to engage with the structural and institutional dynamics that define the advertising ecosystem. This article argues that network analysis—when reimagined beyond its typical application to social media interactions—offers powerful tools for understanding the relationships among brands, consumers, media, influencers, and other intermediaries and stakeholders. Through two case demonstrations, one on influencer marketing and the other on media planning, we illustrate how bipartite projections and inferential network analysis uncover latent relational patterns such as brand competition and influencer heterophily. We further explore alternative strategies for constructing networks from advertising data, including sequence-based, co-occurrence-based, and influence-based approaches. These models enable a shift from descriptive to explanatory analyses and open new pathways for theorizing advertising as a relational, institutional, and dynamic system. Ultimately, we position network analysis as a methodological and theoretical catalyst for structural thinking in advertising research, facilitating deeper engagement with ecosystem-level complexity and interdisciplinary integration with computational social science.
Zhou et al. (Fri,) studied this question.