The responding community is diverse in terms of both geographic distribution and the breadth of interests represented. Responses were received from 29 countries and from 39 states in the United States. Fourteen fields were selected by at least 10 percent of the respondents, and, on average, each respondent selected 3.6 fields. The findings presented in this report do not depend significantly on the field of study or the locale of the respondents but could be limited by the fact that most of them (72 percent) are in the academic community. Further details are given in Appendix D.
Finding 6-3. Seventy percent of the responses to the questionnaire accept the idea that network science is a definable field of investigation.
The questionnaire analysis reveals a widespread but not universal consensus among the respondents that a definable field of network science exists. When the reasons for saying there is no such field as “network science” are analyzed, they break down into five kinds of concern: the phrase has no coherent definition; it is broad to the point of vacuity; it is too early to define such a field; it is merely a new name for already existing fields; or, defining such a field is the wrong approach. In addition, respondents also indicated that the field suffered from excessive hype. The distribution of these responses is shown in Figure 6-1.
Of the responses, 70 percent were affirmative to question Q3a: “Is there an identifiable field of network science?” Twenty-three percent of respondents answered no, and 7 percent did not answer. These percentages show little dependence on the backgrounds of the respondents.
The pervasiveness of dissenter concerns across the responding communities reinforces the need for a clear definition of the field of network science, anchored in the expressed approaches of the researchers involved. It also reinforces the idea that care must be taken not to overstate what is achievable in such a field. More positively, articulating an explicit definition of the term “network science” may address some of these concerns.
The first question in considering a possible field of network science is this: What are its contents and scope? Questions 3a and 3b directly address this issue and provide empirical data on the nature of network science as practiced by current researchers. The responses to four other questions proved highly relevant. Further analysis is presented in Appendix D.
The committee structured its analysis in terms of two basic questions: What are the defining attributes of a network? What are the derived properties of interest? If these questions have answers that are common across many application domains, then network science might be identified as the insights, lexicon, measurements, theories, tools, and techniques that allow one to map between desired output properties of a given network and its input attributes. Mapping is needed in both directions: (1) determining the output properties that arise from specific input attributes and (2) determining the input attributes that could be designed into a new network or achieved by intervention in an existing network in order to realize particular output properties.
If network science is to exist in a meaningful way, these approaches also must be effective over many application domains, with well-understood techniques to apply general tools, methods, and models to specific domains. As a hypothetical example, one might envision a simulation tool that deals with network models across a wide range of size scales and timescales, with a growing suite of model libraries customized to specific application domains—for example, ecological networks, metabolic networks, transportation networks, and so on.
Finding 6-4. Analysis of the responses reveals three common attributes of networks: (1) they consist of nodes connected by links, (2) nodes exchange resources across the links, and (3) nodes only interact through direct linkage.
Few responses captured all three attributes, but all three appear consistently, either explicitly or implicitly (in more domain-specific entries), across a wide range of subject domains. The percentage of responses in which an attribute was mentioned explicitly is indicated in Figure 6-2. For brevity, these attributes are designated “connectivity,” “exchange,” and “locality”:
Connectivity. A network has a well-defined connection topology in which each discrete entity (“node” in graph-theoretic terminology) has a finite number of defined connections (“links”) to other nodes. In general, these links are dynamic.
Exchange. The connection topology exists in order to exchange one or more classes of resource among nodes. Indeed, a link between two nodes exists if and only if resources of significance to the network domain can be directly exchanged between them.
Locality. The exchanged resource is delivered, and its effects take place, only in local interactions (node to link, link to node). This locality of interaction entails autonomous agents acting on a locally available state.
These attributes are discussed in more detail in Appendix D.