Temporal networks provide a framework for modeling systems of interactions that occur between nodes over time. These networks capture both the topological structure of connections as well as the timing of interactions. Three key aspects of temporal networks discussed in the document are:
1) Temporal networks can be represented using contact sequences that capture when interactions occur between nodes, unlike static networks which only represent connections.
2) The temporal structure of interactions, such as patterns in the timing of contacts, can impact dynamical processes unfolding on the network like information or disease spreading.
3) Randomizing the timing of contacts in empirical temporal network data can alter dynamical processes, highlighting the importance of temporal structure beyond just topology.
Building the Social Internet of ThingsBill Harpley
'Building the Social Internet of Things: tools and inspiring ideas for artists and designers' is a call-to-arms for the next generation of artists and designers. It surveys the work of artists who are using data and digital technologies to explore the emerging 'Internet of Things'.
The premise of this presentation is that artists and designers played a critical role in shaping the early commercial Internet of two decades ago.
I think that we face the same challenge today, as we try to make sense of the emerging 'Internet of Everything'. Technologists may like to think that they have all the answers but the truth is that we only understand part of the problem.Once again, we need to call upon the skills of artists and designers to help make the IoE a valuable social phenomenon.
I gave this talk to a group of Fine Arts and Sculpture students at Brighton University in November 2015. They represent the generation that will figure out what the 'Social Internet of Things' will look like. They are the people who will create 'Thingbook'.
Graph Signal Processing: an interpretable framework to link neurocognitive ar...Nicolas Farrugia
This talk attemps to motivate the use of Graph Signal Processing to analyse neuroimaging data. After introducing recent paradigm shifts in neuroimaging research (network neuroscience and principal gradients of connectivity), we present our recent work in combining GSP and machine learning, which show substantial improvements in inference based approach using simple machine learning techniques. We finally open new perspectives regarding the potential of using GSP for interpretable neuroscientific research.
Vibrant Gujarat Summit on e-Governance in IndiaVibrant Gujarat
Make all Government services accessible to the common man in his locality, through common service delivery outlets, and ensure efficiency, transparency, and reliability of such services at affordable costs to realize the basic needs of the common man.
Internet of things are exploding. This whitepaper would help product developers to understand the Security and Privacy issues, their impact and a recommendation for embedding the best practices during PDLC.
Internet of Things is a system of interrelated computing devices, digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
A general overview of Internet of Things and what it can do with the proper example.
The 10 Megatrends of 2022 are the global list of topics that our experts consider will change technology, business models, and society in the medium term. These Megatrends aim to anticipate the answers to the main questions about the future and help us steer our actions and strategies.
Building the Social Internet of ThingsBill Harpley
'Building the Social Internet of Things: tools and inspiring ideas for artists and designers' is a call-to-arms for the next generation of artists and designers. It surveys the work of artists who are using data and digital technologies to explore the emerging 'Internet of Things'.
The premise of this presentation is that artists and designers played a critical role in shaping the early commercial Internet of two decades ago.
I think that we face the same challenge today, as we try to make sense of the emerging 'Internet of Everything'. Technologists may like to think that they have all the answers but the truth is that we only understand part of the problem.Once again, we need to call upon the skills of artists and designers to help make the IoE a valuable social phenomenon.
I gave this talk to a group of Fine Arts and Sculpture students at Brighton University in November 2015. They represent the generation that will figure out what the 'Social Internet of Things' will look like. They are the people who will create 'Thingbook'.
Graph Signal Processing: an interpretable framework to link neurocognitive ar...Nicolas Farrugia
This talk attemps to motivate the use of Graph Signal Processing to analyse neuroimaging data. After introducing recent paradigm shifts in neuroimaging research (network neuroscience and principal gradients of connectivity), we present our recent work in combining GSP and machine learning, which show substantial improvements in inference based approach using simple machine learning techniques. We finally open new perspectives regarding the potential of using GSP for interpretable neuroscientific research.
Vibrant Gujarat Summit on e-Governance in IndiaVibrant Gujarat
Make all Government services accessible to the common man in his locality, through common service delivery outlets, and ensure efficiency, transparency, and reliability of such services at affordable costs to realize the basic needs of the common man.
Internet of things are exploding. This whitepaper would help product developers to understand the Security and Privacy issues, their impact and a recommendation for embedding the best practices during PDLC.
Internet of Things is a system of interrelated computing devices, digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
A general overview of Internet of Things and what it can do with the proper example.
The 10 Megatrends of 2022 are the global list of topics that our experts consider will change technology, business models, and society in the medium term. These Megatrends aim to anticipate the answers to the main questions about the future and help us steer our actions and strategies.
COMMUNICATIONS OF THE ACM November 2004Vol. 47, No. 11 15.docxmonicafrancis71118
COMMUNICATIONS OF THE ACM November 2004/Vol. 47, No. 11 15
N
etworks are hot. The
Internet has made it pos-
sible to observe and mea-
sure linkages
representing relationships of
all kinds. We now recognize
networks everywhere: air
traffic, banking, chemical
bonds, data communications,
ecosystems, finite element
grids, fractals, interstate
highways, journal citations,
material structures, nervous
systems, oil pipelines, orga-
nizational networks, power
grids, social structures, trans-
portation, voice communica-
tion, water supply, Web
URLs, and more.
Several fields are collabo-
rating on the development of
network theory, measurement,
and mapping: mathematics
(graph theory), sociology (net-
works of influence and communi-
cation), computing (Internet), and
business (organizational net-
works). This convergence has pro-
duced useful results for risk
assessment and reduction in com-
plex infrastructure networks,
attacking and defending networks,
protecting against network con-
nectivity failures, operating busi-
nesses, spreading epidemics
(pathogens as well as computer
viruses), and spreading innova-
tion. Here, I will survey the fun-
damental laws of networks that
enable these results.
Defining a Network
A network is usually defined as a
set of nodes and links. The nodes
represent entities such as persons,
machines, molecules, documents,
or businesses; the links represent
relationships between pairs of
entities. A link can be directed
(one-way relationship) or undi-
rected (mutual relationship). A
hop is a transition from one node
to another across a single link
separating them. A path is a series
of hops. Networks are very gen-
eral: they can represent any kind
of relation among entities.
Some common network
topologies (interconnection pat-
terns) have their own names:
clique or island (a connected sub-
network that may be isolated
from other cliques), hierarchical
network (tree structured), hub-
and-spoke network (a special
node, the hub, connected directly
to every other node), and multi-
hub network (several hubs con-
nected directly to many nodes).
Some network topologies are
planned, such as the electric grid,
the interstate highway system, or
Network Laws
M
IC
H
A
EL
S
LO
A
N
Peter J. Denning
Many networks, physical and social, are complex and scale-invariant.
This has important implications from the spread of epidemics and
innovations to protection from attack.
The Profession of IT
16 November 2004/Vol. 47, No. 11 COMMUNICATIONS OF THE ACM
the air traffic system; others are
unplanned. In his seminal papers
about the Internet, Paul Baran
proposed that a planned, distrib-
uted network would be more
resilient to failures than a hub-
and-spoke network.
A host of physical systems eas-
ily fit a network model. Perhaps
less obvious is that human social
networks also fit the model. The
individuals of an organization are
linked by their relationships—
who emails whom, who seeks
advice from whom, or who influ-
ences w.
An information-theoretic, all-scales approach to comparing networksJim Bagrow
My presentation at NetSci 2018 on Portrait Divergence, a new approach to comparing networks that is simple, general-purpose, and easy to interpret.
The preprint: https://arxiv.org/abs/1804.03665
The code: https://github.com/bagrow/portrait-divergence
Delay Tolerant Networking routing as a Game Theory problem – An OverviewCSCJournals
This paper explores the theoretical approach to improve existing Delay and Disruption Tolerant Networking routing algorithms using Game Theory. Game Theory is a systematic study of strategic interaction among rational individuals. DTN deals with networks in challenged environment. DTN focuses on deep space to a broader class of heterogeneous networks that may suffer disruptions, affected by design decisions such as naming and addressing, message formats, data encoding methods, routing, congestion management and security. DTN is part of the Inter Planetary Internet with primary application being deep space networks. The hypothesis behind modeling DTN routing as a game is based on understanding that routing is also a strategic interaction between the DTN nodes. This brings cognitive abilities leading to automated routing decisions.
SIX DEGREES OF SEPARATION TO IMPROVE ROUTING IN OPPORTUNISTIC NETWORKSijujournal
Opportunistic Networks are able to exploit social behavior to create connectivity opportunities. This
paradigm uses pair-wise contacts for routing messages between nodes. In this context we investigated if the
“six degrees of separation” conjecture of small-world networks can be used as a basis to route messages in
Opportunistic Networks. We propose a simple approach for routing that outperforms some popular
protocols in simulations that are carried out with real world traces using ONE simulator. We conclude that
static graph models are not suitable for underlay routing approaches in highly dynamic networks like
Opportunistic Networks without taking account of temporal factors such as time, duration and frequency of
previous encounters.
To have the ability to “think outside the box” is generally regarded as something positive. At a moment in time when resources are scarce, and the problems facing us are many, innovation and professional excellence becomes a requirement, rather than a matter of choice. At the core of our attempts to come up with new, and better solutions are the digital technologies. Within the structural engineering context, the different types of off-the-shelf packages for finite element analysis play a central role. These “black-box” types of software packages exemplify how user-friendliness may have harmful consequences within a field where knowledge and the successful mastery of relevant skills is key, and consequently- ignorance may lead to fatal results. These tools make any effort “venturing outside” difficult to achieve. A technical paradigm shift is called for- that places learning and creative, informed exploration at the heart of the user experience. Presented during the Knowledge Based Engineering session of the 19th IABSE congress entitled "Challenges in Design and Construction of an Innovative and Sustainable Built Environment" held in Stockholm, September 21-23, 2016.
To have the ability to “think outside the box” is generally regarded as something positive. At a moment in time when resources are scarce, and the problems facing us are many, innovation and professional excellence becomes a requirement, rather than a matter of choice. At the core of our attempts to come up with new, and better solutions are the digital technologies. Within the structural engineering context, the different types of off-the-shelf packages for finite element analysis play a central role. These “black-box” types of software packages exemplify how user-friendliness may have harmful consequences within a field where knowledge and the successful mastery of relevant skills is key, and consequently- ignorance may lead to fatal results. These tools make any effort “venturing outside” difficult to achieve. A technical paradigm shift is called for- that places learning and creative, informed exploration at the heart of the user experience. Presented during the Knowledge Based Engineering session of the 19th IABSE congress held in Stockholm, September 21-23, 2016.
New prediction method for data spreading in social networks based on machine ...TELKOMNIKA JOURNAL
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Https://javacoffeeiq.com
Alex Pentland puts it in his productivity study, “fewer memos, more coffee breaks” increases productivity via socialisation and collaboration among staff members.
Similar to Temporal Networks of Human Interaction (20)
Temporal network epidemiology: Subtleties and algorithmsPetter Holme
The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structure’s impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulations—how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them.
This is one segment of a talk where I presented the history of computational social science:
* The origins of computer simulations.
* The trouble to publish computational studies in the 1960s.
* The peak enthusiasm for computer simulations after "Limits of Growth"
* The precursors of social-media data science in the 1980's
Important spreaders in networks: exact results on small graphsPetter Holme
To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.
A paradox of importance in network epidemiologyPetter Holme
Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw
Modeling the fat tails of size fluctuations in organizationsPetter Holme
Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527
From temporal to static networks, and backPetter Holme
Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard either―if we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an “exponential threshold” representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
10. What are we interested in?
Something that can:
. . . be measured relatively easy
(who are involved & when).
. . . and give scientific insights.
Examples:
-Two persons being close to each other.
-Two persons doing things together.
-One person sending a message to another.
Human interaction
11. Two persons being close to each other
-RFID tags.
-Smartphones / Bluetooth.
-Smartphones / GPS.
-Campus Wi-fi.
-Hospital records.
-Co-tagged in images.
-Public transportation.
-Sexual contacts (via Internet mediated prostitution).
Human interaction
12. Two persons doing things together
(not necessarily close)
-Paper co-authorships.
-Movie actors.
-Criminal co-offenders.
One person sending a message to another
-E-mails.
-Internet forums.
-Instant messaging.
Human interaction
34. Time matters
0
0.2
0.4
0.6
0 200 400 600
Time (days)
Empirical
Randomized
800
Fractionofinfectious
1
0.8
0.6
0.4
0.2
0
0 100 200 300
Time (days)
Fractionofinfectious
Rocha, Liljeros, Holme Karsai, et al.
35. Physics Reports 519 (2012) 97–125
Contents lists available at SciVerse ScienceDirect
Physics Reports
journal homepage: www.elsevier.com/locate/physrep
Temporal networks
Petter Holmea,b,c,⇤
, Jari Saramäkid
a
IceLab, Department of Physics, Umeå University, 901 87 Umeå, Sweden
b
Department of Energy Science, Sungkyunkwan University, Suwon 440–746, Republic of Korea
c
Department of Sociology, Stockholm University, 106 91 Stockholm, Sweden
d
Department of Biomedical Engineering and Computational Science, School of Science, Aalto University, 00076 Aalto, Espoo, Finland
a r t i c l e i n f o
Article history:
Accepted 1 March 2012
Available online 6 March 2012
editor: D.K. Campbell
a b s t r a c t
A great variety of systems in nature, society and technology – from the web of sexual
contacts to the Internet, from the nervous system to power grids – can be modeled as
graphs of vertices coupled by edges. The network structure, describing how the graph is
wired, helps us understand, predict and optimize the behavior of dynamical systems. In
many cases, however, the edges are not continuously active. As an example, in networks
of communication via e-mail, text messages, or phone calls, edges represent sequences
of instantaneous or practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can
be represented by a graph where an edge between two individuals is on throughout the
time they are at the same ward. Like network topology, the temporal structure of edge
activations can affect dynamics of systems interacting through the network, from disease
contagion on the network of patients to information diffusion over an e-mail network. In
36. 1
ISBN 978-3-642-36460-0
Understanding Complex Systems
Petter Holme
Jari Saramäki Editors
Temporal
Networks
TemporalNetworksHolme·SaramäkiEds.
Understanding Complex Systems
Petter Holme · Jari Saramäki Editors
Temporal Networks
The concept of temporal networks is an extension of complex networks as a modeling
framework to include information on when interactions between nodes happen. Many
studies of the last decade examine how the static network structure affect dynamic
systems on the network. In this traditional approach the temporal aspects are pre-
encoded in the dynamic system model. Temporal-network methods, on the other hand,
lift the temporal information from the level of system dynamics to the mathematical
representation of the contact network itself. This framework becomes particularly
useful for cases where there is a lot of structure and heterogeneity both in the timings
of interaction events and the network topology. The advantage compared to common
static network approaches is the ability to design more accurate models in order to
explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks
and other spreading phenomena). On the other hand, temporal network methods are
mathematically and conceptually more challenging. This book is intended as a first
introduction and state-of-the art overview of this rapidly emerging field.
Physics
9 7 8 3 6 4 2 3 6 4 6 0 0
39. Randomization
Times shuffled
Original
1
0.8
0.6
0.4
0.2
0
0 100 200 300
Time (days)
Fractionofinfectious
Karsai, et al.,
PRE, 2011.
Random times
Times shuffled
Original
Contact sequences of
links shuffled among
links similar weight
Contact sequences
of links shuffled
1
0.8
0.6
0.4
0.2
0
0 100 200 300
Time (days)
Fractionofinfectious
41. Temporal structure
Fat-tailed interevent time distributions
Slowing down of spreading.
10-12
10
-10
10
-8
10
-6
10
-4
10
-2
10
0
10
2
10
4
10
0
10
1
10
2
10
3
10
4
10
5
10
6
10
7
10
8
Poisson
Power-law
Time
Incidence/N
Min, Goh, Vazquez, 2011. PRE 83, 036102.
But both the cell phone and
the prostitution data are
bursty. So why are they
different w.r.t. spreading?
42.
43.
44. Europhys. Lett., 64 (3), pp. 427–433 (2003)
EUROPHYSICS LETTERS 1 November 2003
Network dynamics of ongoing social relationships
P. Holme(∗
)
Department of Physics, Ume˚a University - 901 87 Ume˚a, Sweden
(received 21 July 2003; accepted in final form 22 August 2003)
PACS. 89.65.-s – Social and economic systems.
PACS. 89.75.Hc – Networks and genealogical trees.
PACS. 89.75.-k – Complex systems.
Abstract. – Many recent large-scale studies of interaction networks have focused on networks
of accumulated contacts. In this letter we explore social networks of ongoing relationships with
an emphasis on dynamical aspects. We find a distribution of response times (times between
consecutive contacts of different direction between two actors) that has a power law shape over a
large range. We also argue that the distribution of relationship duration (the time between the
first and last contacts between actors) is exponentially decaying. Methods to reanalyze the data
to compensate for the finite sampling time are proposed. We find that the degree distribution
for networks of ongoing contacts fits better to a power law than the degree distribution of
the network of accumulated contacts do. We see that the clustering and assortative mixing
coefficients are of the same order for networks of ongoing and accumulated contacts, and that
the structural fluctuations of the former are rather large.
Introduction. – The recent development in database technology has allowed researchers
to extract very large data sets of human interaction sequences. These large data sets are
suitable to the methods and modeling techniques of statistical physics, and thus, the last years
have witnessed the appearance of an interdisciplinary field between physics and sociology [1–3].
More specifically, these studies have focused on network structure —in what ways the networks
45. Limited communication capacity unveils
strategies for human interaction
Giovanna Miritello1,2
, Rube´n Lara2
, Manuel Cebrian3,4
& Esteban Moro1,5
1
Departamento de Matema´ticas & GISC, Universidad Carlos III de Madrid, 28911 Legane´s, Spain, 2
Telefo´nica Research, 28050
Madrid, Spain, 3
NICTA, Melbourne, Victoria 3010, Australia, 4
Department of Computer Science & Engineering, University of
California at San Diego, La Jolla, CA 92093, USA, 5
Instituto de Ingenierı´a del Conocimiento, Universidad Auto´noma de Madrid,
28049 Madrid, Spain.
Connectivity is the key process that characterizes the structural and functional properties of social networks.
However, the bursty activity of dyadic interactions may hinder the discrimination of inactive ties from large
interevent times in active ones. We develop a principled method to detect tie de-activation and apply it to a
large longitudinal, cross-sectional communication dataset (<19 months, <20 million people). Contrary to
the perception of ever-growing connectivity, we observe that individuals exhibit a finite communication
capacity, which limits the number of ties they can maintain active in time. On average men display higher
capacity than women, and this capacity decreases for both genders over their lifespan. Separating
communication capacity from activity reveals a diverse range of tie activation strategies, from stable to
exploratory. This allows us to draw novel relationships between individual strategies for human interaction
and the evolution of social networks at global scale.
any different forces govern the evolution of social relationships making them far from random. In recent
years, the understanding of what mechanisms control the dynamics of activating or deactivating social
SUBJECT AREAS:
SCIENTIFIC DATA
COMPLEX NETWORKS
APPLIED MATHEMATICS
STATISTICAL PHYSICS
Received
15 January 2013
Accepted
2 May 2013
Published
6 June 2013
Correspondence and
requests for materials
should be addressed to
E.M. (emoro@math.
49. SIR on prostitution data
0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfection
Ω
50. SIR on prostitution data
0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfection
Ω
Interevent times neutralized
51. SIR on prostitution data
0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfection
Ω
Beginning times neutralized
52. SIR on prostitution data
0
0.1
0.2
0.3
0.1 0.2 0.90.8 10.70.60.50.40.3
0.1
1
0.01
0.001
per-contact transmission probability
durationofinfection
Ω
End times neutralized
58. Static Temporal
Evaluate by comparing ranking of vertices
Run SIR and measure
the expected outbreak
size Ωi if the i is the
source.
Measure predictors of
i’s importance. (Degree
ki and coreness ci.)
Calculate the rank correlation between Ωi and ki.
Higher correlation → better static representation.
61. R₀ — basic reproductive number,
reproduction ratio, reproductive
ratio, ...
e expected number of secondary
infections of an infectious individual in
a population of susceptible individuals.
62. One of few concepts that
went from mathematical
to medical epidemiology
64. SIR model
ds
dt
= –βsi—
di
dt
= βsi – νi—
= νi
dr
dt
—
S I I I
I R
Ω = r(∞) = 1 – exp[–R₀ Ω]
where R₀ = β/ν
Ω > 0 if and only if R₀ > 1
e epidemic threshold
65. Problems with R₀
Hard to
estimate
Can be hard for
models
& even harder for outbreak data
and many datasets lack
the important early period
e threshold isn’t R₀ = 1 in practice
e meaning of a threshold in a finite population.
In temporal networks, the outbreak size
needn’t be a monotonous function of R₀
66. Problems with R₀
Hard to
estimate
Can be hard for
models
& even harder for outbreak data
and many datasets lack
the important early period
e threshold isn’t R₀ = 1 in practice
e meaning of a threshold in a finite population.
e topic of this project
In temporal networks, the outbreak size
needn’t be a monotonous function of R₀
67. Plan
Use empirical
contact data
Simulate the entire
parameter space of
the SIR model
Plot Ω vs R₀
Figure out what temporal network
structure that creates the deviations
72. avg. fraction of nodes present when 50% of contact happened
avg. fraction of links present when 50% of contact happened
avg. fraction of nodes present at 50% of the sampling time
avg. fraction of links present at 50% of the sampling time
frac. of nodes present 1st and last 10% of the contacts
frac. of links present 1st and last 10% of the contacts
frac. of nodes present 1st and last 10% of the sampling time
frac. of links present 1st and last 10% of the sampling time
Time evolution
degree distribution, mean
degree distribution, s.d.
degree distribution, coefficient of variation
degree distribution, skew
Degree distribution
link duration, mean
link duration, s.d.
link duration, coefficient of variation
link duration, skew
link interevent time, mean
link interevent time, s.d.
link interevent time, coefficient of variation
link interevent time, skew
Link activity
Node activity
node duration, mean
node duration, s.d.
node duration, coefficient of variation
node duration, skew
node interevent time, mean
node interevent time, s.d.
node interevent time, coefficient of variation
node interevent time, skew
Other network structure
number of nodes
clustering coefficient
assortativity
Temporal network structure
73. Correlation between point-cloud shape & temporal
network structure
*
*
** ** ** **
**
*
**
**
**
*
∆R0
0
0.2
0.4
0.6
0.8
1
R²
Time evolution
Node activity Link activity
Degree
distribution
Network
structure
fLT
fNT
fLC
fNC
FLT
FNT
FLC
FNC
γNt
σNt
cNt
µNt
γNτ
σNτ
cNτ
µNτ
γLt
σLt
cLt
µLt
γLτ
σLτ
cLτ
µLτ
γk
σk
ck
µk
N C r
74. ***
**
∆Ω
0
0.2
0.4
0.6
0.8
1
R²
Time evolution
Node activity
Link activity
Network
structure
fLT
fNT
fLC
fNC
FLT
FNT
FLC
FNC
γNt
σNt
cNt
µNt
γNτ
σNτ
cNτ
µNτ
γLt
σLt
cLt
µLt
γLτ
σLτ
cLτ
µLτ
γk
σk
ck
µk
N C r
Degreedistribution
Correlation between point-cloud shape & temporal
network structure
Holme & Masuda, 2015,
PLoS ONE 10:e0120567.
76. Information / opinion spreading
“Viral videos doesn’t spread like viruses.”
Actors does not necessarily get infected by only one other.
Karimi, Holme, 2013. Physica A 392: 3476–3483.
reshold models:
Takaguchi, Masuda, Holme, 2013. PLoS ONE 8: e68629.
Time can be incorporated in many ways.
Major conclusion: burstiness can speed up spreading.
78. ank you!
Collaborators:
Naoki Masuda
Jari Saramäki
Fredrik Liljeros
Luis Rocha
Sungmin Lee
Fariba Karimi
Juan Perotti
Taro Takaguchi
Hang-Hyun Jo
Illustrations by:
Mi Jin Lee