171 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
Publication bias and the canonization of false facts
In the process of scientific inquiry, certain claims accumulate enough
support to be established as facts. Unfortunately, not every claim accorded the
status of fact turns out to be true. In this paper, we model the dynamic
process by which claims are canonized as fact through repeated experimental
confirmation. The community's confidence in a claim constitutes a Markov
process: each successive published result shifts the degree of belief, until
sufficient evidence accumulates to accept the claim as fact or to reject it as
false. In our model, publication bias --- in which positive results are
published preferentially over negative ones --- influences the distribution of
published results. We find that when readers do not know the degree of
publication bias and thus cannot condition on it, false claims often can be
canonized as facts. Unless a sufficient fraction of negative results are
published, the scientific process will do a poor job at discriminating false
from true claims. This problem is exacerbated when scientists engage in
p-hacking, data dredging, and other behaviors that increase the rate at which
false positives are published. If negative results become easier to publish as
a claim approaches acceptance as a fact, however, true and false claims can be
more readily distinguished. To the degree that the model accurately represents
current scholarly practice, there will be serious concern about the validity of
purported facts in some areas of scientific research
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