25,256 research outputs found
Aging and percolation dynamics in a Non-Poissonian temporal network model
We present an exhaustive mathematical analysis of the recently proposed
Non-Poissonian Ac- tivity Driven (NoPAD) model [Moinet et al. Phys. Rev. Lett.,
114 (2015)], a temporal network model incorporating the empirically observed
bursty nature of social interactions. We focus on the aging effects emerging
from the Non-Poissonian dynamics of link activation, and on their effects on
the topological properties of time-integrated networks, such as the degree
distribution. Analytic expressions for the degree distribution of integrated
networks as a function of time are derived, ex- ploring both limits of
vanishing and strong aging. We also address the percolation process occurring
on these temporal networks, by computing the threshold for the emergence of a
giant connected component, highlighting the aging dependence. Our analytic
predictions are checked by means of extensive numerical simulations of the
NoPAD model
Discovering items with potential popularity on social media
Predicting the future popularity of online content is highly important in
many applications. Preferential attachment phenomena is encountered in scale
free networks.Under it's influece popular items get more popular thereby
resulting in long tailed distribution problem. Consequently, new items which
can be popular (potential ones), are suppressed by the already popular items.
This paper proposes a novel model which is able to identify potential items. It
identifies the potentially popular items by considering the number of links or
ratings it has recieved in recent past along with it's popularity decay. For
obtaining an effecient model we consider only temporal features of the content,
avoiding the cost of extracting other features. We have found that people
follow recent behaviours of their peers. In presence of fit or quality items
already popular items lose it's popularity. Prediction accuracy is measured on
three industrial datasets namely Movielens, Netflix and Facebook wall post.
Experimental results show that compare to state-of-the-art model our model have
better prediction accuracy.Comment: 7 pages in ACM style.7 figures and 1 tabl
Epidemic Spreading and Aging in Temporal Networks with Memory
Time-varying network topologies can deeply influence dynamical processes
mediated by them. Memory effects in the pattern of interactions among
individuals are also known to affect how diffusive and spreading phenomena take
place. In this paper we analyze the combined effect of these two ingredients on
epidemic dynamics on networks. We study the susceptible-infected-susceptible
(SIS) and the susceptible-infected-removed (SIR) models on the recently
introduced activity-driven networks with memory. By means of an activity-based
mean-field approach we derive, in the long time limit, analytical predictions
for the epidemic threshold as a function of the parameters describing the
distribution of activities and the strength of the memory effects. Our results
show that memory reduces the threshold, which is the same for SIS and SIR
dynamics, therefore favouring epidemic spreading. The theoretical approach
perfectly agrees with numerical simulations in the long time asymptotic regime.
Strong aging effects are present in the preasymptotic regime and the epidemic
threshold is deeply affected by the starting time of the epidemics. We discuss
in detail the origin of the model-dependent preasymptotic corrections, whose
understanding could potentially allow for epidemic control on correlated
temporal networks.Comment: 10 pages, 8 fogure
Data-driven network alignment
Biological network alignment (NA) aims to find a node mapping between
species' molecular networks that uncovers similar network regions, thus
allowing for transfer of functional knowledge between the aligned nodes.
However, current NA methods do not end up aligning functionally related nodes.
A likely reason is that they assume it is topologically similar nodes that are
functionally related. However, we show that this assumption does not hold well.
So, a paradigm shift is needed with how the NA problem is approached. We
redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment),
which attempts to learn the relationship between topological relatedness and
functional relatedness without assuming that topological relatedness
corresponds to topological similarity, like traditional NA methods do. TARA
trains a classifier to predict whether two nodes from different networks are
functionally related based on their network topological patterns. We find that
TARA is able to make accurate predictions. TARA then takes each pair of nodes
that are predicted as related to be part of an alignment. Like traditional NA
methods, TARA uses this alignment for the across-species transfer of functional
knowledge. Clearly, TARA as currently implemented uses topological but not
protein sequence information for this task. We find that TARA outperforms
existing state-of-the-art NA methods that also use topological information,
WAVE and SANA, and even outperforms or complements a state-of-the-art NA method
that uses both topological and sequence information, PrimAlign. Hence, adding
sequence information to TARA, which is our future work, is likely to further
improve its performance
Relay-Linking Models for Prominence and Obsolescence in Evolving Networks
The rate at which nodes in evolving social networks acquire links (friends,
citations) shows complex temporal dynamics. Preferential attachment and link
copying models, while enabling elegant analysis, only capture rich-gets-richer
effects, not aging and decline. Recent aging models are complex and heavily
parameterized; most involve estimating 1-3 parameters per node. These
parameters are intrinsic: they explain decline in terms of events in the past
of the same node, and do not explain, using the network, where the linking
attention might go instead. We argue that traditional characterization of
linking dynamics are insufficient to judge the faithfulness of models. We
propose a new temporal sketch of an evolving graph, and introduce several new
characterizations of a network's temporal dynamics. Then we propose a new
family of frugal aging models with no per-node parameters and only two global
parameters. Our model is based on a surprising inversion or undoing of triangle
completion, where an old node relays a citation to a younger follower in its
immediate vicinity. Despite very few parameters, the new family of models shows
remarkably better fit with real data. Before concluding, we analyze temporal
signatures for various research communities yielding further insights into
their comparative dynamics. To facilitate reproducible research, we shall soon
make all the codes and the processed dataset available in the public domain
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