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This paper presents a foundational framework for functional modeling of human-robot joint activity in dynamic and unstructured environments. Representing a model of work functions as a network allows for scalable analysis of functional dependencies that create coordination overhead in the human-robot system. Centrality of nodes and cycles in the network can reveal potential patterns of joint activity that point to alternate strategies for human-robot coordination. Analysis of these network structures can provide insight into how a human-robot system may synchronize their activity while managing coordination overhead. We illustrate the use of the framework with a model of collaborative navigation in disaster response, where re-evaluating goals as more information about the environment is identified as a key part of coordination. The modeling capabilities can aid in understanding the effects of coordination strategies and teaming configurations and inform the design of automation capabilities to better support collaborative capabilities.