Revisiting the Foundations of Network Analysis

Authors: Carter T. Butts

Publication, Year: Science, 2009

Link to Paper

Notes by: Matthew R. DeVerna

Revisiting the Foundations of Network AnalysisIntroStandard Framework and Core AssumptionsWhen is a Node a Node?When is an Edge an Edge?Time Scales and Network ProcessesConclusion


Standard Framework and Core Assumptions

Many measurement, analysis, and modeling techniques are rooted within the standard framework. However, when assumptions of this framework do not serve as reasonable approximations of the system of interest, alternative representations and techniques may be necessary. What factors should be considered when choosing a network representation, and what are the consequences when this choose is poorly made?

When is a Node a Node?

To avoid misleading conclusions, the set of nodes should be defined so as to include all distinct entities that are capable of participating in the relationship under study; this definition should be used consistently across networks. Where no such set of entities can be uniquely identified, it is possible that a finite network representation will be inappropriate. An alternative framework (such as a continuous spatial representation) may prove more fruitful. In other cases [such as multilevel processes], simultaneous analysis of the same system at multiple levels of aggregation may be appropriate.

When is an Edge an Edge?

This cannot be resolved solely with better data collection or more elaborate statistical techniques. Rather, one must determine whether the relationship under study is sufficiently stable to be well-approximated by a constant function over the period of interest and whether the values taken by this function across pairs are sufficiently constrained to be approximately dichotomous.

Time Scales and Network Processes

The timing and duration of relationships are critical factors in the susceptibility of the dynamic network to disease transmission, factors that are hidden by the time-aggregated representation. This can be seen in Fig. 1D; for a given network, everyone may become infected or no one may be infected, depending on the edge duration and time of onset.


To represent an empirical phenomenon as a network is a theoretical act. It commits one to assumptions about what is interaction, the nature of that interaction, and the time scale on which that interaction takes place. Such assumptions are not "free," and indeed they can be wrong. Whether studying protein interactions, sexual networks, or computer systems, the appropriate choice of representation is key to getting the correct results.