87 research outputs found
An Analysis of the Production Output Difference Between an 11-pica and a 15-pica Line Width
Considerable research has been done on newspapers in the areas of type sizes, typefaces, leading between lines, readability, legibility and column widths. Tinker and Paterson conducted another study to determine the influence of line width and leading on the speed of reading 9-point type. The results indicate that optimal rate of reading occurs with line widths of 14 to 30 picas and with 1 to 4 pints leading. The Tinker and Paterson studies dealt with reading speed, the Murphy study concerned the proportion of readers who actually read the stories in the alternate forms, and the Hvistendahl study concerned reader estimates of attractiveness and reading speed of type set in two widths. This study deals with the typesetting speed, or the actual amount of written material that can be composed by a line-casting machine operator in two different column widths in a given period of time. It is also the objective of this study to determine the differences in hyphenation between the two measures, and the difference in words set per 100 ems between the two measures, and the difference in words set per 100 ems between the two measures, if such differences exist. Today many newspapers try to reduce typesetting costs by using Linotypes operated automatically by perforated tapes. This operation however, involves an extra step because someone must first punch the tape on another machine, the Tele typesetter perforator. The reason for choosing 15 picas as an ideal column width to compare against the 11-pica column is that The Wall Street Journal and The National Observer are both set on the wider measure
Large-scale structure of time evolving citation networks
In this paper we examine a number of methods for probing and understanding
the large-scale structure of networks that evolve over time. We focus in
particular on citation networks, networks of references between documents such
as papers, patents, or court cases. We describe three different methods of
analysis, one based on an expectation-maximization algorithm, one based on
modularity optimization, and one based on eigenvector centrality. Using the
network of citations between opinions of the United States Supreme Court as an
example, we demonstrate how each of these methods can reveal significant
structural divisions in the network, and how, ultimately, the combination of
all three can help us develop a coherent overall picture of the network's
shape.Comment: 10 pages, 6 figures; journal names for 4 references fixe
Node similarity within subgraphs of protein interaction networks
We propose a biologically motivated quantity, twinness, to evaluate local
similarity between nodes in a network. The twinness of a pair of nodes is the
number of connected, labeled subgraphs of size n in which the two nodes possess
identical neighbours. The graph animal algorithm is used to estimate twinness
for each pair of nodes (for subgraph sizes n=4 to n=12) in four different
protein interaction networks (PINs). These include an Escherichia coli PIN and
three Saccharomyces cerevisiae PINs -- each obtained using state-of-the-art
high throughput methods. In almost all cases, the average twinness of node
pairs is vastly higher than expected from a null model obtained by switching
links. For all n, we observe a difference in the ratio of type A twins (which
are unlinked pairs) to type B twins (which are linked pairs) distinguishing the
prokaryote E. coli from the eukaryote S. cerevisiae. Interaction similarity is
expected due to gene duplication, and whole genome duplication paralogues in S.
cerevisiae have been reported to co-cluster into the same complexes. Indeed, we
find that these paralogous proteins are over-represented as twins compared to
pairs chosen at random. These results indicate that twinness can detect
ancestral relationships from currently available PIN data.Comment: 10 pages, 5 figures. Edited for typos, clarity, figures improved for
readabilit
On the Complexity of Newman's Community Finding Approach for Biological and Social Networks
Given a graph of interactions, a module (also called a community or cluster)
is a subset of nodes whose fitness is a function of the statistical
significance of the pairwise interactions of nodes in the module. The topic of
this paper is a model-based community finding approach, commonly referred to as
modularity clustering, that was originally proposed by Newman and has
subsequently been extremely popular in practice. Various heuristic methods are
currently employed for finding the optimal solution. However, the exact
computational complexity of this approach is still largely unknown.
To this end, we initiate a systematic study of the computational complexity
of modularity clustering. Due to the specific quadratic nature of the
modularity function, it is necessary to study its value on sparse graphs and
dense graphs separately. Our main results include a (1+\eps)-inapproximability
for dense graphs and a logarithmic approximation for sparse graphs. We make use
of several combinatorial properties of modularity to get these results. These
are the first non-trivial approximability results beyond the previously known
NP-hardness results.Comment: Journal of Computer and System Sciences, 201
Uncovering missing links with cold ends
To evaluate the performance of prediction of missing links, the known data
are randomly divided into two parts, the training set and the probe set. We
argue that this straightforward and standard method may lead to terrible bias,
since in real biological and information networks, missing links are more
likely to be links connecting low-degree nodes. We therefore study how to
uncover missing links with low-degree nodes, namely links in the probe set are
of lower degree products than a random sampling. Experimental analysis on ten
local similarity indices and four disparate real networks reveals a surprising
result that the Leicht-Holme-Newman index [E. A. Leicht, P. Holme, and M. E. J.
Newman, Phys. Rev. E 73, 026120 (2006)] performs the best, although it was
known to be one of the worst indices if the probe set is a random sampling of
all links. We further propose an parameter-dependent index, which considerably
improves the prediction accuracy. Finally, we show the relevance of the
proposed index on three real sampling methods.Comment: 16 pages, 5 figures, 6 table
Graph Metrics for Temporal Networks
Temporal networks, i.e., networks in which the interactions among a set of
elementary units change over time, can be modelled in terms of time-varying
graphs, which are time-ordered sequences of graphs over a set of nodes. In such
graphs, the concepts of node adjacency and reachability crucially depend on the
exact temporal ordering of the links. Consequently, all the concepts and
metrics proposed and used for the characterisation of static complex networks
have to be redefined or appropriately extended to time-varying graphs, in order
to take into account the effects of time ordering on causality. In this chapter
we discuss how to represent temporal networks and we review the definitions of
walks, paths, connectedness and connected components valid for graphs in which
the links fluctuate over time. We then focus on temporal node-node distance,
and we discuss how to characterise link persistence and the temporal
small-world behaviour in this class of networks. Finally, we discuss the
extension of classic centrality measures, including closeness, betweenness and
spectral centrality, to the case of time-varying graphs, and we review the work
on temporal motifs analysis and the definition of modularity for temporal
graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and
Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201
Effect of correlations on network controllability
A dynamical system is controllable if by imposing appropriate external
signals on a subset of its nodes, it can be driven from any initial state to
any desired state in finite time. Here we study the impact of various network
characteristics on the minimal number of driver nodes required to control a
network. We find that clustering and modularity have no discernible impact, but
the symmetries of the underlying matching problem can produce linear, quadratic
or no dependence on degree correlation coefficients, depending on the nature of
the underlying correlations. The results are supported by numerical simulations
and help narrow the observed gap between the predicted and the observed number
of driver nodes in real networks
Suppressing cascades of load in interdependent networks
Understanding how interdependence among systems affects cascading behaviors
is increasingly important across many fields of science and
engineering.Inspired by cascades of load shedding in coupled electric grids and
other infrastructure, we study the Bak-Tang-Wiesenfeld sandpile model on
modular random graphs and on graphs based on actual, interdependent power
grids. Starting from two isolated networks, adding some connectivity between
them is beneficial, for it suppresses the largest cascades in each system. Too
much interconnectivity, however, becomes detrimental for two reasons. First,
interconnections open pathways for neighboring networks to inflict large
cascades. Second, as in real infrastructure, new interconnections increase
capacity and total possible load, which fuels even larger cascades. Using a
multitype branching process and simulations we show these effects and estimate
the optimal level of interconnectivity that balances their tradeoffs. Such
equilibria could allow, for example, power grid owners to minimize the largest
cascades in their grid. We also show that asymmetric capacity among
interdependent networks affects the optimal connectivity that each prefers and
may lead to an arms race for greater capacity. Our multitype branching process
framework provides building blocks for better prediction of cascading processes
on modular random graphs and on multi-type networks in general.Comment: Accepted to PNAS Plus. Author summary: 2 pages, 1 figure. Main paper:
13 pages, 7 figures. Supporting information: 7 pages, 10 figure
Emergence of scale-free close-knit friendship structure in online social networks
Despite the structural properties of online social networks have attracted
much attention, the properties of the close-knit friendship structures remain
an important question. Here, we mainly focus on how these mesoscale structures
are affected by the local and global structural properties. Analyzing the data
of four large-scale online social networks reveals several common structural
properties. It is found that not only the local structures given by the
indegree, outdegree, and reciprocal degree distributions follow a similar
scaling behavior, the mesoscale structures represented by the distributions of
close-knit friendship structures also exhibit a similar scaling law. The degree
correlation is very weak over a wide range of the degrees. We propose a simple
directed network model that captures the observed properties. The model
incorporates two mechanisms: reciprocation and preferential attachment. Through
rate equation analysis of our model, the local-scale and mesoscale structural
properties are derived. In the local-scale, the same scaling behavior of
indegree and outdegree distributions stems from indegree and outdegree of nodes
both growing as the same function of the introduction time, and the reciprocal
degree distribution also shows the same power-law due to the linear
relationship between the reciprocal degree and in/outdegree of nodes. In the
mesoscale, the distributions of four closed triples representing close-knit
friendship structures are found to exhibit identical power-laws, a behavior
attributed to the negligible degree correlations. Intriguingly, all the
power-law exponents of the distributions in the local-scale and mesoscale
depend only on one global parameter -- the mean in/outdegree, while both the
mean in/outdegree and the reciprocity together determine the ratio of the
reciprocal degree of a node to its in/outdegree.Comment: 48 pages, 34 figure
Small- and large-scale network structure of live fish movements in Scotland
Networks are increasingly being used as an epidemiological tool for studying the potential for disease transmission through animal movements in farming industries. We analysed the network of live fish movements for commercial salmonids in Scotland in 2003. This network was found to have a mixture of features both aiding and hindering disease transmission, hindered by being fragmented, with comparatively low mean number of connections (2.83), and low correlation between inward and outward connections (0.12), with moderate variance in these numbers (coefficients of dispersion of 0.99 and 3.12 for in and out respectively); but aided by low levels of clustering (0.060) and some non-random mixing (coefficient of assortativity of 0.16). Estimated inter-site basic reproduction number R0 did not exceed 2.4 at high transmission rate. The network was strongly organised into communities, resulting in a high modularity index (0.82). Arc (directed connection) removal indicated that effective surveillance of a small number of connections may facilitate a large reduction in the potential for disease spread within the industry. Useful criteria for identification of these important arcs included degree- and betweenness-based measures that could in future prove useful for prioritising surveillance
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