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Journal of Complex Networks is a peer-reviewed journal published by Oxford University Press, covering Complex Network Analysis Techniques, Opinion Dynamics and Social Influence, Advanced Graph Neural Networks.
| Publisher | Oxford University Press |
|---|---|
| Reference style | Author–year (OUP) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Journal of Complex Networks, 12(3), pp. 45–58.
Formats any DOI in the closest standard style — Journal of Complex Networks 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 Advanced Graph Neural Networks Bioinformatics and Genomic Networks Graph theory and applications |
| ISSN | 2051-1310 |
| Citation impact (2-yr) | 1.67 |
| h-index | 39 |
| i10-index | 173 |
| Total citations | 10,794 |
| Article processing charge | $4,151 |
| Top institutions publishing here | University of Oxford |
| Journal website | comnet.oxfordjournals.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.
In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ‘multilayer’ features into account to try…
Multilayer relationships among entities and information about entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We present open-source software (muxViz) that contains a collection of algorithms for the analysis of multilayer networks, which are an important way to represent a large variety of complex systems throughout science…
Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the…
We survey the concept of assortativity, starting from its original definition by Newman in 2002. Degree assortativity is the most commonly used form of assortativity. Degree assortativity is extensively used in network science. Since degree assortativity alone is not sufficient as a graph analysis tool, assortativity is usually combined with other graph metrics. Much of…
Information cascades are important dynamical processes in complex networks. An information cascade can describe the spreading dynamics of rumour, disease, memes, or marketing campaigns, which initially start from a node or a set of nodes in the network. If conditions are right, information cascades rapidly encompass large parts of the network, thus leading to epidemics…
