28,257 research outputs found
Scaling laws for the movement of people between locations in a large city
Large scale simulations of the movements of people in a ``virtual'' city and
their analyses are used to generate new insights into understanding the dynamic
processes that depend on the interactions between people. Models, based on
these interactions, can be used in optimizing traffic flow, slowing the spread
of infectious diseases or predicting the change in cell phone usage in a
disaster. We analyzed cumulative and aggregated data generated from the
simulated movements of 1.6 million individuals in a computer (pseudo
agent-based) model during a typical day in Portland, Oregon. This city is
mapped into a graph with nodes representing physical locations such
as buildings. Connecting edges model individual's flow between nodes. Edge
weights are constructed from the daily traffic of individuals moving between
locations. The number of edges leaving a node (out-degree), the edge weights
(out-traffic), and the edge-weights per location (total out-traffic) are fitted
well by power law distributions. The power law distributions also fit subgraphs
based on work, school, and social/recreational activities. The resulting
weighted graph is a ``small world'' and has scaling laws consistent with an
underlying hierarchical structure. We also explore the time evolution of the
largest connected component and the distribution of the component sizes. We
observe a strong linear correlation between the out-degree and total
out-traffic distributions and significant levels of clustering. We discuss how
these network features can be used to characterize social networks and their
relationship to dynamic processes.Comment: 18 pages, 10 figure
Beyond Counting: New Perspectives on the Active IPv4 Address Space
In this study, we report on techniques and analyses that enable us to capture
Internet-wide activity at individual IP address-level granularity by relying on
server logs of a large commercial content delivery network (CDN) that serves
close to 3 trillion HTTP requests on a daily basis. Across the whole of 2015,
these logs recorded client activity involving 1.2 billion unique IPv4
addresses, the highest ever measured, in agreement with recent estimates.
Monthly client IPv4 address counts showed constant growth for years prior, but
since 2014, the IPv4 count has stagnated while IPv6 counts have grown. Thus, it
seems we have entered an era marked by increased complexity, one in which the
sole enumeration of active IPv4 addresses is of little use to characterize
recent growth of the Internet as a whole.
With this observation in mind, we consider new points of view in the study of
global IPv4 address activity. Our analysis shows significant churn in active
IPv4 addresses: the set of active IPv4 addresses varies by as much as 25% over
the course of a year. Second, by looking across the active addresses in a
prefix, we are able to identify and attribute activity patterns to network
restructurings, user behaviors, and, in particular, various address assignment
practices. Third, by combining spatio-temporal measures of address utilization
with measures of traffic volume, and sampling-based estimates of relative host
counts, we present novel perspectives on worldwide IPv4 address activity,
including empirical observation of under-utilization in some areas, and
complete utilization, or exhaustion, in others.Comment: in Proceedings of ACM IMC 201
Long-term power-law fluctuation in Internet traffic
Power-law fluctuation in observed Internet packet flow are discussed. The
data is obtained by a multi router traffic grapher (MRTG) system for 9 months.
The internet packet flow is analyzed using the detrended fluctuation analysis.
By extracting the average daily trend, the data shows clear power-law
fluctuations. The exponents of the fluctuation for the incoming and outgoing
flow are almost unity. Internet traffic can be understood as a daily periodic
flow with power-law fluctuations.Comment: 10 pages, 8 figure
Scaling laws of human interaction activity
Even though people in our contemporary, technological society are depending
on communication, our understanding of the underlying laws of human
communicational behavior continues to be poorly understood. Here we investigate
the communication patterns in two social Internet communities in search of
statistical laws in human interaction activity. This research reveals that
human communication networks dynamically follow scaling laws that may also
explain the observed trends in economic growth. Specifically, we identify a
generalized version of Gibrat's law of social activity expressed as a scaling
law between the fluctuations in the number of messages sent by members and
their level of activity. Gibrat's law has been essential in understanding
economic growth patterns, yet without an underlying general principle for its
origin. We attribute this scaling law to long-term correlation patterns in
human activity, which surprisingly span from days to the entire period of the
available data of more than one year. Further, we provide a mathematical
framework that relates the generalized version of Gibrat's law to the long-term
correlated dynamics, which suggests that the same underlying mechanism could be
the source of Gibrat's law in economics, ranging from large firms, research and
development expenditures, gross domestic product of countries, to city
population growth. These findings are also of importance for designing
communication networks and for the understanding of the dynamics of social
systems in which communication plays a role, such as economic markets and
political systems.Comment: 20+7 pages, 4+2 figure
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Global disease monitoring and forecasting with Wikipedia
Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein
and adjust novelty claims accordingly; revise title; various revisions for
clarit
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