12,210 research outputs found
Bursting activity spreading through asymmetric interactions
People communicate with those who have the same background or share a common
interest by using a social networking service (SNS). News or messages propagate
through inhomogeneous connections in an SNS by sharing or facilitating
additional comments. Such human activity is known to lead to endogenous
bursting in the rate of message occurrences. We analyze a multi-dimensional
self-exciting process to reveal dependence of the bursting activity on the
topology of connections and the distribution of interaction strength on the
connections. We determine the critical conditions for the cases where
interaction strength is regulated at either the point of input or output for
each person. In the input regulation condition, the network may exhibit
bursting with infinitesimal interaction strength, if the dispersion of the
degrees diverges as in the scale-free networks. In contrast, in the output
regulation condition, the critical value of interaction strength, represented
by the average number of events added by a single event, is a constant
, independent of the degree dispersion. Thus, the
stability in human activity crucially depends on not only the topology of
connections but also the manner in which interactions are distributed among the
connections.Comment: 8 pages, 8 figure
Reactive point processes: A new approach to predicting power failures in underground electrical systems
Reactive point processes (RPPs) are a new statistical model designed for
predicting discrete events in time based on past history. RPPs were developed
to handle an important problem within the domain of electrical grid
reliability: short-term prediction of electrical grid failures ("manhole
events"), including outages, fires, explosions and smoking manholes, which can
cause threats to public safety and reliability of electrical service in cities.
RPPs incorporate self-exciting, self-regulating and saturating components. The
self-excitement occurs as a result of a past event, which causes a temporary
rise in vulner ability to future events. The self-regulation occurs as a result
of an external inspection which temporarily lowers vulnerability to future
events. RPPs can saturate when too many events or inspections occur close
together, which ensures that the probability of an event stays within a
realistic range. Two of the operational challenges for power companies are (i)
making continuous-time failure predictions, and (ii) cost/benefit analysis for
decision making and proactive maintenance. RPPs are naturally suited for
handling both of these challenges. We use the model to predict power-grid
failures in Manhattan over a short-term horizon, and to provide a cost/benefit
analysis of different proactive maintenance programs.Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks
We consider the problem of analyzing timestamped relational events between a
set of entities, such as messages between users of an on-line social network.
Such data are often analyzed using static or discrete-time network models,
which discard a significant amount of information by aggregating events over
time to form network snapshots. In this paper, we introduce a block point
process model (BPPM) for continuous-time event-based dynamic networks. The BPPM
is inspired by the well-known stochastic block model (SBM) for static networks.
We show that networks generated by the BPPM follow an SBM in the limit of a
growing number of nodes. We use this property to develop principled and
efficient local search and variational inference procedures initialized by
regularized spectral clustering. We fit BPPMs with exponential Hawkes processes
to analyze several real network data sets, including a Facebook wall post
network with over 3,500 nodes and 130,000 events.Comment: To appear at The Web Conference 201
Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception
Cyber deception is emerging as a promising approach to defending networks and
systems against attackers and data thieves. However, despite being relatively
cheap to deploy, the generation of realistic content at scale is very costly,
due to the fact that rich, interactive deceptive technologies are largely
hand-crafted. With recent improvements in Machine Learning, we now have the
opportunity to bring scale and automation to the creation of realistic and
enticing simulated content. In this work, we propose a framework to automate
the generation of email and instant messaging-style group communications at
scale. Such messaging platforms within organisations contain a lot of valuable
information inside private communications and document attachments, making them
an enticing target for an adversary. We address two key aspects of simulating
this type of system: modelling when and with whom participants communicate, and
generating topical, multi-party text to populate simulated conversation
threads. We present the LogNormMix-Net Temporal Point Process as an approach to
the first of these, building upon the intensity-free modeling approach of
Shchur et al. to create a generative model for unicast and multi-cast
communications. We demonstrate the use of fine-tuned, pre-trained language
models to generate convincing multi-party conversation threads. A live email
server is simulated by uniting our LogNormMix-Net TPP (to generate the
communication timestamp, sender and recipients) with the language model, which
generates the contents of the multi-party email threads. We evaluate the
generated content with respect to a number of realism-based properties, that
encourage a model to learn to generate content that will engage the attention
of an adversary to achieve a deception outcome
Correlated bursts and the role of memory range
Inhomogeneous temporal processes in natural and social phenomena have been
described by bursts that are rapidly occurring events within short time periods
alternating with long periods of low activity. In addition to the analysis of
heavy-tailed inter-event time distributions, higher-order correlations between
inter-event times, called correlated bursts, have been studied only recently.
As the possible mechanisms underlying such correlated bursts are far from being
fully understood, we devise a simple model for correlated bursts by using a
self-exciting point process with variable memory range. Here the probability
that a new event occurs is determined by a memory function that is the sum of
decaying memories of the past events. In order to incorporate the noise and/or
limited memory capacity of systems, we apply two memory loss mechanisms, namely
either fixed number or variable number of memories. By using theoretical
analysis and numerical simulations we find that excessive amount of memory
effect may lead to a Poissonian process, which implies that for memory effect
there exists an intermediate range that will generate correlated bursts of
magnitude comparable to empirical findings. Hence our results provide deeper
understanding of how long-range memory affects correlated bursts.Comment: 9 pages, 7 figure
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