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Network Science is a peer-reviewed journal published by Cambridge University Press, covering Complex Network Analysis Techniques, Opinion Dynamics and Social Influence, Diverse Scientific and Economic Studies. Over its lifetime it has published 469 papers, which have received 5,602 citations.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Network Science 12 (2023) 45–58.
Formats any DOI in the closest standard style — Network Science has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Complex Network Analysis Techniques Opinion Dynamics and Social Influence Diverse Scientific and Economic Studies Legal Cases and Commentary Mental Health Research Topics |
| ISSN | 2050-1242 |
| Citation impact (2-yr) | 1.24 |
| h-index | 35 |
| i10-index | 146 |
| Top institutions publishing here | Indiana University Bloomington |
| Journal website | www.cambridge.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This is the beginning of Network Science . The journal has been created because network science is exploding. As is typical for a field in formation, the discussions about its scope, contents, and foundations are intense. On these first few pages of the first issue of our new journal, we would like to share…
Abstract Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real…
Abstract Empirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are…
Homophily - the tendency for individuals to associate with similar others - is one of the most persistent findings in social network analysis. Its importance is established along the lines of a multitude of sociologically relevant dimensions, e.g. sex, ethnicity and social class. Existing research, however, mostly focuses on one dimension at a time. But…
Abstract Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying…
