Networks provide connections and positions that influence health outcomes. Social network analysis examines relationships between actors to understand how networks impact behavior. Networks matter through both connectionist mechanisms like diffusion, and positional mechanisms like social roles. Network data can be analyzed at different levels from individual ego networks to global networks, and can involve one or multiple types of relationships between nodes. Social network data is commonly represented through matrices and lists to encode network structure and allow computational analysis.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
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.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
2. 1. Intro/Big Picture
1. What are networks?
2. Connections & Positions
2. Network Relevance to Health Research
3. Basic Network Data Elements
1. Types of networks
2. Levels of analysis
3. Data structures
Outline
Social Network Data
3. Introduction
We live in a connected world:
“To speak of social life is to speak of the association between
people – their associating in work and in play, in love and in
war, to trade or to worship, to help or to hinder. It is in the
social relations men establish that their interests find
expression and their desires become realized.”
Peter M. Blau
Exchange and Power in Social Life, 1964
4. *1934, NYTime. Moreno claims this work was covered in “all the major papers” but I can’t find any other clips…
*
Introduction
We live in a connected world:
"If we ever get to the point of charting a whole city or a whole nation, we would have … a picture
of a vast solar system of intangible structures, powerfully influencing conduct, as gravitation does
in space. Such an invisible structure underlies society and has its influence in determining the
conduct of society as a whole."
J.L. Moreno, New York Times, April 13, 1933
5. But scientists are starting to take network seriously:
“Networks”
Introduction
11. And yet, standard social science analysis methods do not take this space
into account.
“For the last thirty years, empirical social research has been
dominated by the sample survey. But as usually practiced, …, the
survey is a sociological meat grinder, tearing the individual from his
social context and guaranteeing that nobody in the study interacts
with anyone else in it.”
Allen Barton, 1968 (Quoted in Freeman 2004)
Moreover, the complexity of the relational world makes it impossible to
identify social connectivity using only our intuition.
Social Network Analysis (SNA) provides a set of tools to empirically
extend our theoretical intuition of the patterns that compose social
structure.
Introduction
12. Social network analysis is:
•a set of relational methods for systematically understanding
and identifying connections among actors. SNA
•is motivated by a structural intuition based on ties
linking social actors
•is grounded in systematic empirical data
•draws heavily on graphic imagery
•relies on the use of mathematical and/or computational
models.
•Social Network Analysis embodies a range of theories
relating types of observable social spaces and their relation
to individual and group behavior.
Introduction
13. Social Determinants of Health
“…social determinants of health refers to the complex, integrated, and overlapping
social structures and economic systems that include social and physical environments
and health services.” (CDC, 2010)
WHO Commission on Social Determinants of Health Conceptual Framework
Introduction
14. Social Determinants of Health
Social factors matter
RWJ, Health Affairs (2014) “The relative contributions of multiple determinants to health outcomes”
Introduction
15. Social Determinants of Health
Social effects hold promising multiplier effects:
Introduction
32. Mark S. Handcock, David R. Hunter,
Carter T. Butts, Steven M. Goodreau, and
Martina Morris (2003).
statnet: Software tools for the Statistical
Modeling of Network Data. URL
http://statnetproject.org
33.
34.
35.
36.
37.
38. State of the field
Trends
English language Articles indexed in Web of Science Social
Science Citation Index on: ("health" or "well being" or
"medicine") and "network*").
There have been 18572 such papers since 2000.
39. State of the field
Big-Picture
Bibliographic Similarity Networks: 1-step neighborhood of a single paper
40. State of the field
Big-Picture
Bibliographic Similarity Networks: 2-step neighborhood of a single paper
41. State of the field
Big-Picture
Since the net is large…
Use a force-directed layout to display the full space & overlay clusters….
60. Introduction
Key Questions
Social Network analysis lets us answer questions about social interdependence.
These include:
“Networks as Variables” approaches
•Are kids with smoking peers more likely to smoke themselves?
•Do unpopular kids get in more trouble than popular kids?
•Do central actors control resources?
“Networks as Structures” approaches
•What generates hierarchy in social relations?
•What network patterns spread diseases most quickly?
•How do role sets evolve out of consistent relational activity?
Both: Connectionist vs. Positional features of the network
We don’t want to draw this line too sharply: emergent role positions can
affect individual outcomes in a ‘variable’way, and variable approaches
constrain relational activity.
61. Why do networks matter?
Two fundamental mechanisms: Problem space
Connectionist:
Positional:
Networks as pipes
Networks as roles
Networks
As Cause
Networks
As Result
Diffusion
Peer influence
Social Capital
“small worlds”
Social integration
Peer selection
Homophily
Network robustness
Popularity Effects
Role Behavior
Network Constraint
Group stability
Network ecology
“Structuration”
This rubric is organized around social mechanisms – the reasons why networks matter,
which ends up being loosely correlated with specific types of measures, analysis, and
data collection method.
62. Why do networks matter?
Two fundamental mechanisms: Connections
Connectionist network mechanisms : Networks matter because of the
things that flow through them. Networks as pipes.
C
P
X Y
63. The spread of any epidemic depends on the number of
secondary cases per infected case, known as the
reproductive rate (R0). R0 depends on the probability that
a contact will be infected over the duration of contact (b),
the likelihood of contact (c), and the duration of
infectiousness (D).
cDRo b
For network transmission problems, the trick is specifying c,
which depends on the network.
C
P
X Y
Why do networks matter?
Two fundamental mechanisms: Connections example
64. Isolated visionWhy do networks matter?
Two fundamental mechanisms: Connections example
C
P
X Y
65. Connected visionWhy do networks matter?Why do networks matter?
Two fundamental mechanisms: Connections example
C
P
X Y
66. Partner
Distribution
Component
Size/Shape
Emergent Connectivity in “low-degree” networks
C
P
X Y
Connections: Diffusion
Example: Small local changes can create cohesion
cascades
Based on work supported by R21-HD072810 (NICHD, Moody PI), R01 DA012831-05 (NIDA Morris, Martina PI)
67. Provides food for
Romantic Love
Bickers with
Why do networks matter?
Two fundamental mechanisms: Positions
Positional network mechanisms : Networks matter because of the way they
capture role behavior and social exchange. Networks as Roles.
C
P
X Y
68. Parent Parent
Child
Child
Child
Provides food for
Romantic Love
Bickers with
Why do networks matter?
Two fundamental mechanisms: Positions
Positional network mechanisms : Networks matter because of the way they
capture role behavior and social exchange. Networks as Roles.
C
P
X Y
69. The unit of interest in a network are the combined sets of
actors and their relations.
We represent actors with points and relations with lines.
Actors are referred to variously as:
Nodes, vertices or points
Relations are referred to variously as:
Edges, Arcs, Lines, Ties
Example:
a
b
c e
d
Social Network Data
70. In general, a relation can be:
Binary or Valued
Directed or Undirected
a
b
c e
d
Undirected, binary Directed, binary
a
b
c e
d
a
b
c e
d
Undirected, Valued Directed, Valued
a
b
c e
d
1 3
4
21
Social Network Data
71. In general, a relation can be: (1) Binary or Valued (2) Directed or Undirected
Social Network Data
Basic Data Elements
The social process of interest will often determine what form your data take. Conceptually, almost
all of the techniques and measures we describe can be generalized across data format, but you may
have to do some of the coding work yourself….
a
b
c e
d
Directed,
Multiplex categorical edges
72. We can examine networks across multiple levels:
1) Ego-network
- Have data on a respondent (ego) and the people they are connected to
(alters). Example: 1985 GSS module
- May include estimates of connections among alters
2) Partial network
- Ego networks plus some amount of tracing to reach contacts of
contacts
- Something less than full account of connections among all pairs of
actors in the relevant population
- Example: CDC Contact tracing data for STDs
Social Network Data
Basic Data Elements: Levels of analysis
73. 3) Complete or “Global” data
- Data on all actors within a particular (relevant) boundary
- Never exactly complete (due to missing data), but boundaries are set
-Example: Coauthorship data among all writers in the social
sciences, friendships among all students in a classroom
We can examine networks across multiple levels:
Social Network Data
Basic Data Elements: Levels of analysis
75. Social Network Data
Social network data are substantively divided by the number of
modes in the data.
1-mode data represents edges based on direct contact between
actors in the network. All the nodes are of the same type (people,
organization, ideas, etc). Examples:Communication, friendship,
giving orders, sending email.
This is commonly
what people think
about when
thinking about
networks: nodes
having direct
relations with
each other.
76. Social Network Data
Social network data are substantively divided by the number of
modes in the data.
2-mode data represents nodes from two separate classes, where
all ties are across classes. Examples:
People as members of groups
People as authors on papers
Words used often by people
Events in the life history of people
The two modes of the data represent a duality: you can project
the data as people connected to people through joint membership
in a group, or groups to each other through common membership
There may be multiple relations of multiple types connecting
your nodes.
77. Bipartite networks imply a constraint on the mixing, such that ties only cross classes.
Here we see a tie connecting each woman with the party she attended (Davis data)
Social Network Data
Basic Data Elements: Modes
78. Social Network Data
Basic Data Elements: Modes
Bipartite networks imply a constraint on the mixing, such that ties only cross classes.
Here we see a tie connecting each woman with the party she attended (Davis data)
79. By projecting the data, one can look at the shared between people or the common
memberships in groups: this is the person-to-person projection of the 2-mode data.
Social Network Data
Basic Data Elements: Modes
80. Social Network Data
Basic Data Elements: Modes
By projecting the data, one can look at the shared between people or the common
memberships in groups: this is the group-to-group projection of the 2-mode data.
81. Social Network Data
Example of a 2-mode
network: faculty
supervising students
- Any list of what
people do –
meetings, clubs,
activities, co-
authorship, – that
they do with others
forms network data.
Moody
82. The Movement of Carbapenem-Resistant Klebsiella pneumoniae among Healthcare Facilities: A Network Analysis
D van Duin, F Perez, E Cober, SS Richter, RC Kalayjian, RA Salata, N Scalera, R Watkins, Y Doi, S Evans, VG Fowler Jr, KS Kaye, SD Rudin, KM Hujer, AM Hujer,
RA Bonomo, and J Moody for the Antibacterial Resistance Leadership Group
Social Network Data
Example of a 2-mode network: Patients & Care Settings
83. Casalino, Lawrence P., Michael F. Pesko, Andrew M. Ryan, David J. Nyweide, Theodore J. Iwashyna, Xuming Sun, Jayme Mendelsohn and James
Moody. “Physician Networks and Ambulatory Care Admissions” Medical Care 53:534-41
Social Network Data
Example of a 2-mode network: Patients & Care Settings
84. From pictures to matrices
a
b
c e
d
Undirected, binary
a b c d e
a
b
c
d
e
1
1 1
1 1 1
1 1
1 1
An undirected graph and the
corresponding matrix is symmetric.
The traditional way to store & represent
network data is with an adjacency matrix.
The matrix (X) at right represents an
undirected binary network. Each node (a-e)
is listed on both the row and the column.
The ith row and the jth column (Xij) records the
value of a tie from node i to node j. For
example, the line between nodes a and b is
represented as an entry in the first row and
second column (red at right).
Because the graph is undirected the ties sent
are the same as the ties receive, so every entry
above the diagonal equals the entries below
the diagonal.
Basic Data Structures
Social Network Data
85. Directed, binary
a
b
c e
d
a b c d e
a
b
c
d
e
1
1
1 1 1
1 1
A directed graph and the
corresponding matrix is asymmetrical.
Directed graphs, on the other hand,
are asymmetrical.
We can see that Xab =1 and Xba =1,
therefore a “sends” to b and b “sends” to a.
However, Xbc=0 while Xcb=1; therefore,
c “sends” to b, but b does not reciprocate.
Basic Data Structures
Social Network Data
86. a b c d e
a
b
c
d
e
1
3
1 2 4
2 1
A directed graph and the
corresponding matrix is asymmetrical.
Directed graphs, on the other hand,
are asymmetrical.
We can see that Xab =1 and Xba =1,
therefore a “sends” to b and b “sends” to a.
However, Xbc=0 while Xcb=1; therefore,
c “sends” to b, but b does not reciprocate.
Basic Data Structures
Social Network Data
Directed, Valued
a
b
c e
d
87. From matrices to lists (binary)
a b c d e
a
b
c
d
e
1
1 1
1 1 1
1 1
1 1
a b
b a c
c b d e
d c e
e c d
a b
b a
b c
c b
c d
c e
d c
d e
e c
e d
Adjacency List
Arc List
Social network analysts also use adjacency lists and arc lists
to more efficiently store network data.
a
b
c e
d
Basic Data Structures
Social Network Data
88. From matrices to lists (valued)
a b c d e
a
b
c
d
e
1
1 2
2 3 5
3 1
5 1
a b
b a c
c b d e
d c e
e c d
a b 1
b a 1
b c 2
c b 2
c d 3
c e 5
d c 3
d e 1
e c 5
e d 1
Adjacency List
Arc List
Social network analysts also use adjacency lists and arc lists
to more efficiently store network data.
a
b
c e
d
Basic Data Structures
Social Network Data
1 2
5
13 a 1
b 1 2
c 2 3 1
d 3 1
e 5 1
contact value
89. Working with two-mode data
A person-to-group adjacency matrix is rectangular, with one mode
(persons, say) down rows and the other (groups, say) across columns
1 2 3 4 5
A 0 0 0 0 1
B 1 0 0 0 0
C 1 1 0 0 0
D 0 1 1 1 1
E 0 0 1 0 0
F 0 0 1 1 0
A =
Each column is a group,
each row a person, and
the cell = 1 if the person in
that row belongs to that
group.
You can tell how many
groups two people both
belong to by comparing
the rows: Identify every
place that both rows = 1,
sum them, and you have
the overlap.
Basic Data Structures
Social Network Data