You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review 
Chapter Title:  Temporal measures: 
1. Abstract of Cited Paper (BIB001):  Many networks are dynamic in that their topology changes rapidly--on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

Literature Review:
As we saw in the previous sections, the constructed networks were in many times aggregated over time and the static measures were applied, which weakens the validity of the results or simply turns them not meaningful BIB002 . Thus, we recommend using the dynamic networks and their corresponding measures as provided by Kim et al. BIB002 and by Holme et al. BIB001 . We strongly think that utilizing temporal networks as a model to understand the dynamics of collaboration and its effect would give more actionable insights.