Interaction Patterns and Collective Outcomes in Network Systems
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Understanding how interaction patterns shape collective outcomes in network systems is a critical issue across disciplines such as social science, economics, and engineering. This dissertation explores two different instances of this phenomenon: the aggregation of opinions in multi-community populations and the emergence of higher-order network structures through subgraph generation. More in detail, the first study examines the propagation of opinion dynamics in mixed communities, focusing on how the selection of a committee can affect opinion aggregation in populations involving two communities (a majority and a minority), with different initial opinions. Our work provides theoretical results characterizing the effect of a committee, in terms of its size and composition, on the final opinion in multi-community populations. The second study considers Subgraph Generated Models (SUGM), a framework proposed by Chandrasekhar and Jackson (2016) that extends traditional random network models by incorporating higher-order structures, such as triangles and cliques into the network formation process. Our work demonstrates how key network characteristics of sampled networks can be predicted by using the generating model. This is advantageous in situations in which the exact network of interactions is not available, yet a generating model can be inferred. Together, these studies provide a perspective on how network structures influence the behavior of complex multi-agent systems and how practitioners can analyze such systems by means of heterogeneous and stochastic network models.