4,499 research outputs found
Patterns of Interactions in Complex Social Networks Based on Coloured Motifs Analysis
Coloured network motifs are small subgraphs that enable to discover and interpret the patterns of interaction within the complex networks. The analysis of three-nodes motifs where the colour of the node reflects its high – white node or low – black node centrality in the social network is presented in the paper. The importance of the vertices is assessed by utilizing two measures: degree prestige and degree centrality. The distribution of motifs in these two cases is compared to mine the interconnection patterns between nodes. The analysis is performed on the social network derived from email communication
Bridging topological and functional information in protein interaction networks by short loops profiling
Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship
From calls to communities: a model for time varying social networks
Social interactions vary in time and appear to be driven by intrinsic
mechanisms, which in turn shape the emerging structure of the social network.
Large-scale empirical observations of social interaction structure have become
possible only recently, and modelling their dynamics is an actual challenge.
Here we propose a temporal network model which builds on the framework of
activity-driven time-varying networks with memory. The model also integrates
key mechanisms that drive the formation of social ties - social reinforcement,
focal closure and cyclic closure, which have been shown to give rise to
community structure and the global connectedness of the network. We compare the
proposed model with a real-world time-varying network of mobile phone
communication and show that they share several characteristics from
heterogeneous degrees and weights to rich community structure. Further, the
strong and weak ties that emerge from the model follow similar weight-topology
correlations as real-world social networks, including the role of weak ties.Comment: 10 pages, 5 figure
Correlated dynamics in egocentric communication networks
We investigate the communication sequences of millions of people through two
different channels and analyze the fine grained temporal structure of
correlated event trains induced by single individuals. By focusing on
correlations between the heterogeneous dynamics and the topology of egocentric
networks we find that the bursty trains usually evolve for pairs of individuals
rather than for the ego and his/her several neighbors thus burstiness is a
property of the links rather than of the nodes. We compare the directional
balance of calls and short messages within bursty trains to the average on the
actual link and show that for the trains of voice calls the imbalance is
significantly enhanced, while for short messages the balance within the trains
increases. These effects can be partly traced back to the technological
constrains (for short messages) and partly to the human behavioral features
(voice calls). We define a model that is able to reproduce the empirical
results and may help us to understand better the mechanisms driving technology
mediated human communication dynamics.Comment: 7 pages, 6 figure
Detection of Epigenomic Network Community Oncomarkers
In this paper we propose network methodology to infer prognostic cancer
biomarkers based on the epigenetic pattern DNA methylation. Epigenetic
processes such as DNA methylation reflect environmental risk factors, and are
increasingly recognised for their fundamental role in diseases such as cancer.
DNA methylation is a gene-regulatory pattern, and hence provides a means by
which to assess genomic regulatory interactions. Network models are a natural
way to represent and analyse groups of such interactions. The utility of
network models also increases as the quantity of data and number of variables
increase, making them increasingly relevant to large-scale genomic studies. We
propose methodology to infer prognostic genomic networks from a DNA
methylation-based measure of genomic interaction and association. We then show
how to identify prognostic biomarkers from such networks, which we term
`network community oncomarkers'. We illustrate the power of our proposed
methodology in the context of a large publicly available breast cancer dataset
The use of multilayer network analysis in animal behaviour
Network analysis has driven key developments in research on animal behaviour
by providing quantitative methods to study the social structures of animal
groups and populations. A recent formalism, known as \emph{multilayer network
analysis}, has advanced the study of multifaceted networked systems in many
disciplines. It offers novel ways to study and quantify animal behaviour as
connected 'layers' of interactions. In this article, we review common questions
in animal behaviour that can be studied using a multilayer approach, and we
link these questions to specific analyses. We outline the types of behavioural
data and questions that may be suitable to study using multilayer network
analysis. We detail several multilayer methods, which can provide new insights
into questions about animal sociality at individual, group, population, and
evolutionary levels of organisation. We give examples for how to implement
multilayer methods to demonstrate how taking a multilayer approach can alter
inferences about social structure and the positions of individuals within such
a structure. Finally, we discuss caveats to undertaking multilayer network
analysis in the study of animal social networks, and we call attention to
methodological challenges for the application of these approaches. Our aim is
to instigate the study of new questions about animal sociality using the new
toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl
Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections
According to the Eurobarometer report about EU media use of May 2018, the
number of European citizens who consult on-line social networks for accessing
information is considerably increasing. In this work we analyze approximately
tweets exchanged during the last Italian elections. By using an
entropy-based null model discounting the activity of the users, we first
identify potential political alliances within the group of verified accounts:
if two verified users are retweeted more than expected by the non-verified
ones, they are likely to be related. Then, we derive the users' affiliation to
a coalition measuring the polarization of unverified accounts. Finally, we
study the bipartite directed representation of the tweets and retweets network,
in which tweets and users are collected on the two layers. Users with the
highest out-degree identify the most popular ones, whereas highest out-degree
posts are the most "viral". We identify significant content spreaders by
statistically validating the connections that cannot be explained by users'
tweeting activity and posts' virality by using an entropy-based null model as
benchmark. The analysis of the directed network of validated retweets reveals
signals of the alliances formed after the elections, highlighting commonalities
of interests before the event of the national elections
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