178 research outputs found
An information-theoretic framework for resolving community structure in complex networks
To understand the structure of a large-scale biological, social, or
technological network, it can be helpful to decompose the network into smaller
subunits or modules. In this article, we develop an information-theoretic
foundation for the concept of modularity in networks. We identify the modules
of which the network is composed by finding an optimal compression of its
topology, capitalizing on regularities in its structure. We explain the
advantages of this approach and illustrate them by partitioning a number of
real-world and model networks.Comment: 5 pages, 4 figure
Why ex post peer review encourages high-risk research while ex ante review discourages it
Peer review is an integral component of contemporary science. While peer
review focuses attention on promising and interesting science, it also
encourages scientists to pursue some questions at the expense of others. Here,
we use ideas from forecasting assessment to examine how two modes of peer
review -- ex ante review of proposals for future work and ex post review of
completed science -- motivate scientists to favor some questions instead of
others. Our main result is that ex ante and ex post peer review push
investigators toward distinct sets of scientific questions. This tension arises
because ex post review allows an investigator to leverage her own scientific
beliefs to generate results that others will find surprising, whereas ex ante
review does not. Moreover, ex ante review will favor different research
questions depending on whether reviewers rank proposals in anticipation of
changes to their own personal beliefs, or to the beliefs of their peers. The
tension between ex ante and ex post review puts investigators in a bind,
because most researchers need to find projects that will survive both. By
unpacking the tension between these two modes of review, we can understand how
they shape the landscape of science and how changes to peer review might shift
scientific activity in unforeseen directions.Comment: 11 pages, 4 figures, 1 appendix. Version 2 includes revamped notation
and some text edits to the discussio
Mapping change in large networks
Change is a fundamental ingredient of interaction patterns in biology,
technology, the economy, and science itself: Interactions within and between
organisms change; transportation patterns by air, land, and sea all change; the
global financial flow changes; and the frontiers of scientific research change.
Networks and clustering methods have become important tools to comprehend
instances of these large-scale structures, but without methods to distinguish
between real trends and noisy data, these approaches are not useful for
studying how networks change. Only if we can assign significance to the
partitioning of single networks can we distinguish meaningful structural
changes from random fluctuations. Here we show that bootstrap resampling
accompanied by significance clustering provides a solution to this problem. To
connect changing structures with the changing function of networks, we
highlight and summarize the significant structural changes with alluvial
diagrams and realize de Solla Price's vision of mapping change in science:
studying the citation pattern between about 7000 scientific journals over the
past decade, we find that neuroscience has transformed from an
interdisciplinary specialty to a mature and stand-alone discipline.Comment: 10 pages, 4 figure
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