MAT381E-Fall21
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+    <title>MAT381E-Week 10: Creating and Visualizing Networks</title>
+    <meta charset="utf-8" />
+    <meta name="author" content="Gül İnan" />
+    <meta name="date" content="2022-01-10" />
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+
+# MAT381E-Week 10: Creating and Visualizing Networks
+### Gül İnan
+### Department of Mathematics<br/>Istanbul Technical University
+### January 10, 2022
+
+---
+
+
+
+
+
+
+
+
+
+
+
+
+
+class: left
+
+# Outline
+
+* What is Network?
+ * Examples
+* Elements of a graph. 
+* The `tidygraph` and `ggraph` packages.  
+---
+
+class: center, middle
+
+Internet - the largest engineering project
+
+&lt;iframe width="700" height="500" src="https://www.youtube.com/embed/DdaElt6oP6w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen&gt;&lt;/iframe&gt;
+
+[Source](https://www.opte.org/)
+
+---
+class: middle, center
+
+&lt;img src="images/internet.png" width="100%" height="100%" /&gt;
+
+[Source](https://www.opte.org/about)
+---
+
+# Networks
+- **Network analysis** is a suite of tools/methods developed to understand and analyze graphs in which several **vertices** (nodes) are connected each other through  **edges**.
+- With an example from Facebook network, we can consider **vertices** are **users** in the network and **edges** are links denoting **friendships**.
+
+
+&lt;img src="images/fb.jpeg" width="40%" height="40%" style="display: block; margin: auto;" /&gt;
+---
+#### Networks are everywhere!..
+
+- In real life, network analysis helps us to understand complex relationships,
+learn about behaviors, preferences, and trends.
+
+---
+#### Example: History
+&lt;style type="text/css"&gt;
+.pull-left {
+  float: left;
+  width: 50%;
+}
+.pull-right {
+  float: right;
+  width: 50%;
+}
+&lt;/style&gt;
+
+.pull-left[
+&lt;blockquote class="twitter-tweet"&gt;&lt;p lang="en" dir="ltr"&gt;The exhibition “From Reformation to the Republic: Master Artists, Artist Students,&amp;quot; which presents the interaction and change between generations through its focus on the relationship between masters and their apprentices, is at Sakıp Sabancı Museum.⁣⁣⁣ &lt;a href="https://t.co/HEN9LhG9Yy"&gt;pic.twitter.com/HEN9LhG9Yy&lt;/a&gt;&lt;/p&gt;&amp;mdash; Sakıp Sabancı Müzesi (@SSabanciMuze) &lt;a href="https://twitter.com/SSabanciMuze/status/1404764702618619913?ref_src=twsrc%5Etfw"&gt;June 15, 2021&lt;/a&gt;&lt;/blockquote&gt; &lt;script async src="https://platform.twitter.com/widgets.js" charset="utf-8"&gt;&lt;/script&gt;
+]
+
+.pull-right[
+&lt;blockquote class="twitter-tweet"&gt;&lt;p lang="tr" dir="ltr"&gt;“Yüksek Osmanlı Ulemasının Mesleki ve Entelektüel İlişki Ağları ve Gruplaşmaları (1470-1650)” başlıklı projemizi nihayet bitirdik. İzninizle proje konusu hakkında kısa bir bilgi vermeyi ve birkaç örnek paylaşmayı istiyorum. &lt;a href="https://t.co/PFJmfGV5j7"&gt;pic.twitter.com/PFJmfGV5j7&lt;/a&gt;&lt;/p&gt;&amp;mdash; Abdurrahman Atçıl (@aatcil) &lt;a href="https://twitter.com/aatcil/status/1307022765363474435?ref_src=twsrc%5Etfw"&gt;September 18, 2020&lt;/a&gt;&lt;/blockquote&gt; &lt;script async src="https://platform.twitter.com/widgets.js" charset="utf-8"&gt;&lt;/script&gt;
+]
+---
+#### Example: Job Connectivity in US
+
+&lt;img src="images/example.png" width="75%" height="100%" /&gt;
+
+[Source](https://news.mit.edu/2021/job-connectivity-improves-resiliency-us-cities-0413)
+
+---
+#### Example continu'ed
+- "Economists, policymakers, city planners, and companies have a strong interest in determining what factors contribute to healthy job markets, including what factors can help promote faster recovery after a shock, such as a major recession or the current Covid-19 pandemic." 
+- "Traditional modeling approaches in this realm have treated **workers as narrowly linked to specific jobs**. 
+- "In the real world, however, **jobs and sectors are linked**. &lt;span style="color:red"&gt;Displaced workers can often transition to another job or sector requiring similar skills.&lt;/span&gt;"
+- "In this way, **job markets are much like ecosystems, where organisms are linked in a complex web of relationships**.
+- "The authors modeled the relationships between jobs in cities across the United States. They predicted that **cities with jobs connected by overlapping skills and geography** would fare better in the face of economic shock than those without such networks."
+- "They found that while cities of similar sizes would be affected similarly in the beginning phases of automation shocks, those with **well-connected job networks** would provide **better opportunities** for displaced workers to find other jobs." 
+- "This provides a buffer against widespread unemployment, and in some cases even leads to **more jobs** being created in the aftermath of the initial automation shock."
+- A city like Burlington, Vermont, where job connectivity is high, would fare much better than Bloomington, Indiana, a similar-sized city where job connectivity is low.
+- "The findings of the study suggest that **policymakers should consider job connectivity** when planning for the future of work in their regions, especially where automation is expected to replace large numbers of jobs." 
+- "Furthermore, in individual occupations, workers in jobs that are more “embedded” (connected to other jobs) in a region **earn higher wages** than similar workers in areas where those jobs are not as connected."
+
+---
+&lt;style type="text/css"&gt;
+.pull-left {
+  float: left;
+  width: 50%;
+}
+.pull-right {
+  float: right;
+  width: 50%;
+}
+&lt;/style&gt;
+
+#### Example: Open Syllabus Co-assignment Galaxy
+- An interactive visualization of the underlying “co-assignment graph” – the network of relationships among books and articles formed by aggregating over all pairs of titles that appear together in the same courses.
+
+.pull-left[
+&lt;img src="images/open_syllabus.png" width="100%" height="100%" /&gt;
+]
+
+
+
+.pull-right[
+&lt;img src="images/syl.png" width="100%" height="100%" /&gt;
+]
+
+[Source 1](https://galaxy.opensyllabus.org/#!viewport/5.5826/21.4688/-7.8283/0.0187)
+[Source 2](https://blog.opensyllabus.org/galaxy-v2)
+
+---
+#### Example: Covid-19 Knowledge Graph
+
+&lt;img src="images/covidgraph.png" width="80%" height="100%" /&gt;
+
+[Source](https://covidgraph.org/)
+
+---
+&lt;style type="text/css"&gt;
+.pull-left {
+  float: left;
+  width: 50%;
+}
+.pull-right {
+  float: right;
+  width: 50%;
+}
+&lt;/style&gt;
+
+#### Example: Covid-19 Bibliometric Analysis
+
+.pull-left[
+&lt;img src="images/bib1.png" width="100%" height="100%" /&gt;
+]
+
+
+.pull-right[
+&lt;img src="images/bib2.png" width="100%" height="100%" /&gt;
+]
+
+[Source](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396244/)
+
+---
+#### More at: Stanford Large Network Dataset Collection
+
+&lt;img src="images/stanford.png" width="100%" height="100%" /&gt;
+[Source](https://snap.stanford.edu/data/index.html)
+
+---
+#### Popular Network Analysis Software
+- Network analysis and visualization tools are mostly implemented either in a **specific software with a GUI** such as:
+   - [Gephi](https://gephi.org/) and
+   - [Cytoscape](https://cytoscape.org/)
+- or in a **package/library within a programming language** such as:
+   - [igraph](https://igraph.org/) (R/Python library),
+   - [tidygraph](https://cran.r-project.org/web/packages/tidygraph/index.html) (R library),
+   - [ggraph](https://cran.r-project.org/web/packages/ggraph/index.html) (R library), and
+   - [NetworkX](https://networkx.org/) (Python library).
+---
+#### Graph Data Bases
+- There also graph databases such as [Neo4j graph data base](https://neo4j.com/) which stores the relationship between data records in a graph format and allows users to do queries with complex connections.
+
+&lt;img src="images/nasa.png" width="100%" height="100%" /&gt;
+---
+- Some free books to download from web:
+
+.pull-left[
+&lt;img src="images/graphds.png" width="80%" height="100%" /&gt;
+[Source](https://neo4j.com/graph-data-science-for-dummies/?ref=home)
+]
+
+.pull-right[
+&lt;img src="images/graph_apache.png" width="80%" height="100%" /&gt;
+[Source](https://neo4j.com/graph-data-science-for-dummies/?ref=home)
+]
+
+---
+- Research Institutes
+
+&lt;a href="https://www.networkscienceinstitute.org/" target="_blank"&gt;&lt;img src="images/northeastern.png" width="80%" height="100%" style="display: block; margin: auto;" /&gt;&lt;/a&gt;
+
+---
+- More is available at:
+  - [Handbook of Graphs and Networks in People Analytics](https://ona-book.org/gitbook/).
+  - [Awesome list curated by François Briatte](https://github.com/briatte/awesome-network-analysis).
+  - [Katya Ognyanova's Network Visualization with R](http://kateto.net/network-visualization).
+  - [Douglas A. Luke, *A User’s Guide to Network Analysis in R* (2015)](http://www.springer.com/us/book/9783319238821).
+  - [Eric D. Kolaczyk and Gábor Csárdi's, Statistical Analysis of Network Data with R (2014)](http://www.springer.com/us/book/9781493909827).
+
+---
+# Attributions
+- All images used in this slide are taken from the web.
+- This lecture note is mainly developed by following sources:
+   - [Source 1](http://veronikarock.com/teaching/06_slides.pdf),
+   - [Source 2](https://www.jessesadler.com/post/network-analysis-with-r/),
+   - [Source 3](http://users.dimi.uniud.it/~massimo.franceschet/ns/syllabus/make/tidygraph/tidygraph.html),
+   - [Source 4](https://towardsdatascience.com/notes-on-graph-theory-centrality-measurements-e37d2e49550a), and
+   - [Source 5](https://networkingarchives.github.io/blog/2021/04/15/my-network-analysis-workflow/).
+
+
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@@ -4,7 +4,7 @@
     <title>MAT381E-Week 10: Creating and Visualizing Networks</title>
     <meta charset="utf-8" />
     <meta name="author" content="Gül İnan" />
-    <meta name="date" content="2021-12-13" />
+    <meta name="date" content="2022-01-10" />
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@@ -28,7 +28,7 @@
 # MAT381E-Week 10: Creating and Visualizing Networks
 ### Gül İnan
 ### Department of Mathematics<br/>Istanbul Technical University
-### December 13, 2021
+### January 10, 2022
 
 ---