Understanding how research areas emerge can help identify nascent areas early and inform research strategy, yet how the intellectual base of a field restructures as an area takes shape remains unclear. We hypothesize that the emergence of a research area is accompanied by the integration of largely separate knowledge communities, observable as a decline in the modularity of its co-citation network, which represents its intellectual base. We propose a framework that tracks this modularity over time, evaluates the statistical robustness of its changes, and identifies the papers highly associated with the decline. We applied it to three areas with different modes of growth: higher-order network science, superstring theory, and graph representation learning. In all three, modularity declined in correspondence with each area's emergence or transformation, and in superstring theory, the decline aligns with an independently documented transition. Further analysis of higher-order network science shows that its decline reflects a cross-disciplinary integration. In graph representation learning, the gradual decline is followed by a rise, which we interpret as a re-differentiation after the emergence period. Our results suggest that a decline in the modularity of a co-citation network can serve as a structural signature that retrospectively characterizes this integrative mode of emergence.

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