For the full course see: https://goo.gl/iehZHU
Follow along with the course eBook: https://goo.gl/i8sfGP
The way in which a network is connected plays a large part in how we will analyze and interpret it. When analyzing connectedness and clustering we are asking how integrated or fractured the overall network system is, how these different major sub-systems are distributed out and their local characteristics.
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Transcription excerpt:
The way in which a network is connected plays a large part in how we will analyze and interpret it. When analyzing connectedness and clustering we are asking how integrated or fractured the overall network system is, how these different major sub-systems are distributed out and their local characteristics.
A graph can said to be connected if for any node in the graph there is a path to any other node, when the graph is not connected then there will be a number of what we call components to it. A component is a sub-set of nodes and edges within a graph that are fully connected, thus for a node to be part of a component it must be connected to all the other nodes in that component.
A cluster is simply a subset of the nodes and edges in a graph that possess certain common characteristics, or relate to each other in a particular ways forming some domain-specific structure. So where as a component is simply referring to whether a given set of nodes are all connected or not, a cluster is referring to how they are connected and how much they are connected that is the frequency of links between a given subset of nodes. In order to model the degree of clustering of a subset of nodes we simply take a node and look at how connect a node it links to is to other nodes that it is also connected to. So if this was a social network of friends we would be asking how many of your friends know your other friends, the more your friends are interconnect the more clustered the subset is said to be. This clustering within social networks is also called a clique, a clique is a group of people who interact with each other more regularly and intensely than others in the same setting.
Within this social context clustering can be correlated to homophily, where homophily describes the phenomenon where people tend to form connections with those similar to themselves, as captured in the famous saying “birds of a feather flock together”. We might think of clustering coming from the fact that the interaction between nodes with similar attributes will often require less resources than interaction between nodes with different attributes, for example between to cultures there may be a language barrier or between different devices on a network that might have different protocols, or clustering may be due to physical constraints of the resource expenditure required to maintain them over a greater distance, thus resulting in a clustering around a geographic neighborhood.
Understanding the different local conditions that have created clustering within a network are important for understanding why the network is distributed out into the topology that it has, how you can work to integrate it or disintegrate it and how something will propagate across the network, as each one of these clusters will have its own unique set of properties within the whole making it particularly receptive or resistant to a given phenomena. For example we might be analyzing a political network, with each cluster in this network representing a different set of ideologies, social values and policy agendas that are receptive to different messages.
Or as another example by understanding that different clustering groups on a computer network may represent different operating systems we will be able to better understand why a virus has rapidly spread in one part of the network but not in another and also by understanding these local clustering condition we will be able to better approach integrating them into the broader network.
The clustering coefficient of a node is then a method for measuring the degree of a local cluster, there are a number of such methods for measuring this but they are basically trying to capture the ratio of existing links connecting a nodes neighbors to each other relative to the maximum possible number of such links that could exist between them. A high clustering coefficient for a network is another indication of this small-world phenomena that we saw previously.

Views: 14848
Complexity Labs

DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ
What is clustering or degree distribution, and how do they affect our interpretation of what’s going on in a network? We define these terms in this video. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center.
Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php
Duke Network Analysis Center: https://dnac.ssir.duke.edu

Views: 1640
Mod•U: Powerful Concepts in Social Science

Learn about clustering in networks

Views: 594
Social and Economic Networks

Triads, clustering coefficient and neighborhood overlap

Views: 3074
Social Networks

Tselil Schramm (Simons Institute, UC Berkeley)
One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective.
-------------------
References from the Whiteboard:
Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral
clustering: Analysis and an algorithm." Advances in neural information
processing systems. 2002.
Lee, James R., Shayan Oveis Gharan, and Luca Trevisan. "Multiway
spectral partitioning and higher-order cheeger inequalities." Journal
of the ACM (JACM) 61.6 (2014): 37.
-------------------
Additional Resources:
In my explanation of the spectral embedding I roughly follow the exposition from the lectures of Dan Spielman (http://www.cs.yale.edu/homes/spielman/561/), focusing on the content in lecture 2. Lecture 1 also contains some additional striking examples of graphs and their spectral embeddings.
I also make some imprecise statements about the relationship between the spectral embedding and the minimum-energy configurations of a mass-spring system. The connection is discussed more precisely here (https://www.simonsfoundation.org/2012/04/24/network-solutions/).
License: CC BY-NC-SA 4.0
- https://creativecommons.org/licenses/by-nc-sa/4.0/

Views: 5570
GraphXD: Graphs Across Domains

This video explains you about "What is Cluster? Why do we need Cluster? what are the types of Clusters? and Understand the Basic Cluster Concepts for Beginners".
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#cluster #highavailabilty #loadbalancer
cluster tutorial, cluster tutorial for beginners, clustering tutorial, server clustering tutorial, linux cluster tutorial, cluster concepts, cluster basics, cluster video, cluster tutorial videos, cluster basic concepts, basic cluster concepts, how cluster works, introduction to cluster, introduction to clustering, clustering tutorials, understand cluster concepts, cluster concepts for beginners, high availability cluster tutorial, server clustering concepts, clustering tutorials

Views: 109185
LearnITGuide Tutorials

An introduction to social network analysis and network structure measures, like density and centrality.
Table of Contents:
00:00 - Network Structure
00:12 - Degree Distribution
02:42 - Degree Distribution
06:17 - Density
10:31 - Clustering Coefficient
11:24 - Which Node is Most Important?
12:10 - Which Node is Most Important?
13:27 - Closeness Centrality
15:01 - Closeness Centrality
16:17 - Closeness Centrality
16:36 - Degree Centrality
17:33 - Betweenness Centrality
17:53 - Betweenness Centrality
20:55 - Eigenvector Centrality
23:02 - Connectivity and Cohesion
24:24 - Small Worlds
26:28 - Random Graphs and Small Worlds

Views: 60461
jengolbeck

Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

Views: 13282
nptelhrd

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses clustering.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 75288
MIT OpenCourseWare

Graph density. Graph pertitioning. Min cut, ratio cut, normalized and quotient cuts metrics. Spectral graph partitioning (normalized cut). Direct (spectral) modularity maximization. Multilevel recursive partitioning
Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture9.pdf

Views: 8517
Leonid Zhukov

In this video, I will be introducing my multipart series on clustering algorithms. I introduce clustering, and cover various types of clusterings. Check back soon for part 2.
Credit for much of the information used to make this video must go to "Introduction to Data Mining" by Pang-Ning Tan, Michael Steinbach and Vipin Kumar. I refer to the first edition, published in 2006.

Views: 7166
Laurel Powell

What's a computer cluster? How does it work? How does it handle work for a whole lot of people at once?

Views: 72234
Ross Dickson

Provides illustration of doing cluster analysis with R.
R File: https://goo.gl/BTZ9j7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- Illustrates the process using utilities data
- data normalization
- hierarchical clustering using dendrogram
- use of complete and average linkage
- calculation of euclidean distance
- silhouette plot
- scree plot
- nonhierarchical k-means clustering
Cluster analysis is an important tool related to analyzing big data or working in data science field.
Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 98427
Bharatendra Rai

Clustering A Network Topology
Lecture By: Mr. Shakthi Swaroop, Tutorials Point India Private Limited

Views: 89
Tutorials Point (India) Pvt. Ltd.

Hierarchical Clustering - Fun and Easy Machine Learning with Examples
►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp
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Hierarchical Clustering
Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left.
The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree
Difference between K Means and Hierarchical clustering
Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2).
In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering.
K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D).
K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram.
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Views: 25816
Augmented Startups

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 18900
Noureddin Sadawi

Basics of Social Network Analysis
In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research.
The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit
http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php
The video's content includes:
Why Social Network Analysis (SNA)?
Enables us to segment data based on user behavior.
Understand natural groups that have formed:
a. topics
b. personal characteristics
Understand who are the important people in these groups.
Analysing Social Networks:
Data Collection Methods:
a. Surveys
b. Interviews
c. Observations
Analysis:
a. Computational analysis of matrices
Relationships:
A. is connected to B.
SNA Introduction:
[from] A. Directed Graph [to] B. e.g. Twitter replies and mentions
A. Undirected Graph B. e.g. family relationships
What is Social Network Analysis?
Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)).
Social Network Analysis Basics: Node and Edge
Node:
“actor” or people on which relationships act
Edge:
relationship connecting nodes; can be directional
Social Network Analysis Basics: Cohesive Sub-group
Cohesive Sub-group:
a. well-connected group, clique, or cluster, e.g. A, B, D, and E
Social Network Analysis Basics: Key Metrics
Centrality (group or individual measure):
a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only).
b. Measure at the individual node or group level.
Cohesion (group measure):
a. Ease with which a network can connect.
b. Aggregate measure of shortest path between each node pair at network level reflects average distance.
Density (group measure):
a. Robustness of the network.
b. Number of connections that exist in the group out of 100% possible.
Betweenness (individual measure):
a. Shortest paths between each node pair that a node is on.
b. Measure at the individual node level.
Social Network Analysis Basics: Node Roles:
Node Roles:
Peripheral – below average centrality, e.g. C.
Central connector – above average centrality, e.g. D.
Broker – above average betweenness, e.g. E.
References and Reading
Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492.
Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8.
Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.

Views: 36098
Alexandra Ott

( Data Science Training - https://www.edureka.co/data-science )
This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

Views: 60773
edureka!

Dragan Gasevic discusses network modularity and community identification for week 3 of DALMOOC.

Views: 14078
Data Analytics and Learning MOOC

Full lecture: http://bit.ly/K-means
The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.

Views: 478491
Victor Lavrenko

K-means clustering is used in all kinds of situations and it's crazy simple. Example R code in on the StatQuest website: https://statquest.org/2017/07/05/statquest-k-means-clustering/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

Views: 44353
StatQuest with Josh Starmer

This is a walkthrough of the IBM weka tutorials covering regression and clustering
https://www.ibm.com/developerworks/library/os-weka1/
https://www.ibm.com/developerworks/library/os-weka2/
https://www.ibm.com/developerworks/library/os-weka3/

Views: 9279
jengolbeck

Get a Free Trial: https://goo.gl/C2Y9A5
Get Pricing Info: https://goo.gl/kDvGHt
Ready to Buy: https://goo.gl/vsIeA5
Cluster iris flowers based on petal and sepal size. For more videos, visit http://www.mathworks.com/products/neural-network/examples.html

Views: 17607
MATLAB

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/
And please support me on Patreon:
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Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

Views: 87500
Siraj Raval

.
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

Views: 10291
Artificial Intelligence - All in One

Networks encode complex information on all kinds of interactions. We look at how a network perspective can reveal valuable information about corruption in public procurement, internal collaboration at a multinational firm, and the tone of campaigns on Twitter, all with the phenomenal NetworkX library. NetworkX is a highly productive and actively maintained library that interacts well with other libraries and environments. It inherits many Python strengths like fast prototyping and ease of teaching. We also discuss alternatives like graph-tool and igraph.

Views: 15040
PyCon SK

Views: 20397
Omar Sobh

.
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

Views: 13681
Artificial Intelligence - All in One

SSAS - Data Mining - Decision Trees, Clustering, Neural networks

Views: 1168
M R Dhandhukia

Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in DWM
To buy the course click here: https://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
[email protected]
or
[email protected]
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 321378
Last moment tuitions

Illustrative video for the Medium Article 'Visualizing My Network Clusters'. Made with Gephi.

Views: 837
Ashris Choudhury

.
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

Views: 6519
Artificial Intelligence - All in One

Karl Rohe, University of Wisconsin-Madison
Unifying Theory and Experiment for Large-Scale Networks http://simons.berkeley.edu/talks/karl-rohe-2013-11-19

Views: 711
Simons Institute

Dr. Debra Knisley, ETSU Department of Mathematics and Statistics
MATH 5840 - Complex Networks and Systems
ETSU Online Programs - http://www.etsu.edu/online

Views: 119
East Tennessee State University

Social network analysis with several simple examples in R.
R file: https://goo.gl/CKUuNt
Data file: https://goo.gl/Ygt1rg
Includes,
- Social network examples
- Network measures
- Read data file
- Create network
- Histogram of node degree
- Network diagram
- Highlighting degrees & different layouts
- Hub and authorities
- Community detection
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 16614
Bharatendra Rai

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

Views: 5379
Udacity

To enroll in my introductory Power BI course:
https://www.udemy.com/learn-power-bi-fast/?couponCode=CHEAPEST

Views: 3128
BI Elite

Improving clustering by imposing network information. Susanne Gerber and Illia Horenko (2015), Science Advances http://dx.doi.org/10.1126/sciadv.1500163
Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface.

Views: 39
ScienceVio

For full courses see: https://goo.gl/JJHcsw
Follow along with the course eBook: https://goo.gl/Z2ekrB
A brief overview to the new area of social network analysis that applies network theory to the analysis of social relations.
Produced by: http://complexitylabs.io
Twitter: https://goo.gl/ZXCzK7
Facebook: https://goo.gl/P7EadV
LinkedIn: https://goo.gl/3v1vwF
Transcription:
Social network analysis is the application of network theory to the modeling and analysis of social systems. it combine both tools for analyzing social relations and theory for explaining the structures that emerge from the social interactions.
Of course the idea of studying societies as networks is not a new one but with the rise in computation and the emergence of a mass of new data sources, social network analysis is beginning to be applied to all type and scales of social systems from, international politics to local communities and everything in between.
Traditionally when studying societies we think of them as composed of various types of individuals and organizations, we then proceed to analysis the properties to these social entities such as their age, occupation or population, and them ascribe quantitative value to them.
This allows social science to use the formal mathematical language of statistical analyst to compare the values of these properties and create categories such as low in come house holds or generation x, we then search for quasi cause and effect relations that govern these values.
This component-based analysis is a powerful method for describing social systems. Unfortunately though is fails to capture the most important feature of social reality that is the relations between individuals, statistical analysis present a picture of individuals and groups isolates from the nexus of social relations that given them context.
Thus we can only get so far by studying the individual because when individuals interact and organize, the results can be greater than the simple sum of its parts, it is the relations between individuals that create the emergent property of social institutions and thus to understand these institutions we need to understand the networks of social relations that constitute them.
Ever since the emergence of human beans we have been building social networks, we live our lives embed in networks of relations, the shape of these structures and where we lie in them all effect our identity and perception of the world.
A social network is a system made up of a set of social actors such as individuals or organizations and a set of ties between these actors that might be relations of friendship, work colleagues or family. Social network science then analyze empirical data and develops theories to explaining the patterns observed in these networks
In so doing we can begin to ask questions about the degree of connectivity within a network, its over all structure, how fast something will diffuse and propagate through it or the Influence of a given node within the network. lets take some examples of this
Social network analysis has been used to study the structure of influence within corporations, where traditionally we see organization of this kind as hierarchies, by modeling the actual flow of information and communication as a network we get a very different picture, where seemingly irrelevant employees within the hierarchy can in fact have significant influence within the network.
Researcher also study innovation as a process of diffusion of new ideas across networks, where the oval structure to the network, its degree of connectivity, centralization or decentralization are a defining feature in the way that innovation spreads or fails to spread.
Network dynamics, that is how networks evolve overtime is another important area of research, for example within Law enforcement agencies social network analysis is used to study the change in structure of terrorists groups to identify changing relations through which they are created, strengthened and dissolved?
Social network analysis has also been used to study patterns of segregation and clustering within international politics and culture, by mapping out the beliefs and values of countries and cultures as networks we can identify where opinions and beliefs overlap or conflict.
Social network analysis is a powerful new method we now have that allows us to convert often large and dense data sets into engaging visualization, that can quickly and effectively communicate the underlining dynamics within the system.
By combine new discoveries in the mathematics of network theory, with new data sources and our sociological understanding, social network analysis is offering huge potential for a deeper, richer and more accurate understanding, of the complex social systems that make up our world.

Views: 39166
Complexity Labs

Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.

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Esri Events

PROJECTS9-more than 5000 projects
if you want this projects click on below link www.projects9.com

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projectsnine

Erdos-Reni random graph model. Poisson and Bernulli distributions. Distribution of node degrees. Phase transition, gigantic connected component. Diameter and cluster coefficient. Configuration model
Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture3.pdf

Views: 10303
Leonid Zhukov

A quick tutorial on how to use gephi's modularity feature to detect communities and color code them in graphs.

Views: 54974
jengolbeck

Tutorial on cluster analysis of incident points in ArcGIS 10.2 for GPH904 at Salem State University
Interested in learning more from me? Salem State University offers a Bachelor of Science in Cartography and GIS. We also offer a graduate Certificate and a Master of Science in Geo-Information Science. Learn more at https://www.salemstate.edu/academics/colleges-and-schools/college-arts-and-sciences/geography

Views: 20991
Marcos Luna

MIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
http://compbio.mit.edu/6.047/
Fall 2018
Lecture 6- Gene expression analysis: Clustering and Classification
1. Introduction to gene expression analysis
- Technology: microarrays vs. RNAseq. Resulting data matrices
- Supervised (Clustering) vs. unsupervised (classification) learning
2. K-means clustering (clustering by partitioning)
- Algorithmic formulation: Update rule, optimality criterion. Fuzzy k-means.
- Machine learning formulation: Generative models, Expectation Maximization.
3. Hierarchical Clustering (clustering by agglomeration)
- Basic algorithm, Distance measures. Evaluating clustering results
4. Naïve Bayes classification (generative approach to classification)
- Discriminant function: class priors, and class-conditional distributions
- Training and testing, Combine mult features, Classification in practice
5. (optional) Support Vector Machines (discriminative approach)
- SVM formulation, Margin maximization, Finding the support vectors
- Non-linear discrimination, Kernel functions, SVMs in practice
Slides for Lecture 6:
https://stellar.mit.edu/S/course/6/fa18/6.047/courseMaterial/topics/topic2/lectureNotes/Lecture06_ExpressionClust---ication4_thin.pptx/Lecture06_ExpressionClust---Classification_6up.pdf

Views: 373
Manolis Kellis