Added notes on Lesson 1

OMSCS/Courses/NS/L01 - Intro Notes.md

@@ -0,0 +1,182 @@
+---
+tags:
+  - OMSCS
+  - NS
+---
+# L01 - Intro Notes
+
+Course textbook is an online resource
+- Errata: http://networksciencebook.com/translations/en/resources/NetworkScienceErrata.pdf
+- Book: http://networksciencebook.com/
+
+## Knowledge Quiz
+- Network science is the study of complex systems through their network representation.
+- The network architecture of a complex system is not sufficient to understand the system's functions and dynamics.
+- Centrality
+	- Centrality metrics aim to rank nodes (or edges) based on "importance"
+	- There are many metrics/algorithms for defining/finding centrality
+	- In ring networks, all nodes have the same centrality
+	- Pagerank is a good example of an algorithm/system which ranks nodes in a network by importance
+- dynamics on networks describe a process through which the state of network nodes changes over time even if the network topology is static
+
+## What is Network Science?
+ > _The study of complex systems focusing on their architecture, i.e., on the network, or graph, that shows how the system components are interconnected._
+ 
+- Many and heterogeneous components
+- Components that interact with each other through a _(non-trivial)_ network
+- Non-linear interactions between components
+
+## Network Complexity/Topology
+![[Pasted image 20240517174214.png]]
+
+![[Pasted image 20240519060119.png]]
+
+- regular networks
+	- rings
+	- cliques
+	- lattices
+- random networks
+	- connections between nodes are determined randomly
+- most technological, biological, and information systems do not have a regular/random architecture
+- a major difference between network science and graph theory
+	- network science is an applied data-science discipline that focuses on complex networks encountered in real-world systems
+	- graph theory is a mathematical field that focuses mostly on regular/random graphs
+
+## The brain of a C.elegans Worm
+- worm is 1mm in length
+- roughly 300 neurons
+- has many standard animal behaviors
+- Its been fully mapped using various techniques, and network science allows you to analyze it
+
+## Main Premise
+> even if we don’t know every little detail about a system and its components, simply knowing the map or “wiring diagram” that shows how the different system components are interconnected provides sufficient information to answer a lot of important questions about that system.
+
+> if our goal is to design a new system (rather than analyze an existing system), network science suggests that we should first start from its network representation, and only when that is completely done, move to lower-level design and implementation.
+
+![[Pasted image 20240519060119.png]]
+
+> Suppose that we are to design a communication system of some sort that will interconnect 6 sites. The first question is: what should be the network architecture?
+
+- Line
+	- cheapest
+	- vulnerable to disconnects
+	- inefficient
+- Ring
+	- strict upgrade over line
+	- only slightly more expensive
+- Fully connected
+	- most expensive
+	- efficient
+	- resilient
+- Mesh
+	- good balance of tradeoffs
+
+## Examples in the wild
+- [Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks](https://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/teensex.pdf)
+-  [Rise of China in the International Trade Network: A Community Core Detection Approach](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138169/)
+- [Predicting the Fission Yeast Protein Interaction Network](https://pdfs.semanticscholar.org/0595/6042c6bd23eb49b5071964ce2d04edb26921.pdf?_ga=2.238092041.829074902.1579584841-796840842.1579584841)
+- [Networking Our Way to Better Ecosystem Service Provision](https://www.sciencedirect.com/science/article/pii/S0169534715003006)
+- [Influence of fake news in Twitter during the 2016 US presidential election](https://www.nature.com/articles/s41467-018-07761-2)​
+- [Wireless Data Center with Millimeter Wave Network](https://ieeexplore.ieee.org/document/5684121) 
+- [Data visualization for social network analysis](https://cambridge-intelligence.com/use-cases/social-networks/)​
+- [The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations](https://link.springer.com/article/10.1007/s00265-003-0651-y)
+- [The synapse, Khan Academy](https://www.khanacademy.org/science/biology/human-biology/neuron-nervous-system/a/the-synapse)​
+- [Action potentials and synapses, The University of Queensland](https://qbi.uq.edu.au/brain-basics/brain/brain-physiology/action-potentials-and-synapses)​
+- [Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment](https://www.frontiersin.org/articles/10.3389/fnagi.2018.00404/full)
+- [The Measurement Standard, Carma](http://www.themeasurementstandard.com/wp-content/uploads/2016/04/network-of-swords.jpg%E2%80%8B)
+- [Schizophrenia interactome with 504 novel protein–protein interactions](https://www.nature.com/articles/npjschz201612%E2%80%8B)
+
+## Network Centrality
+![[Pasted image 20240519060747.png]]
+
+- co-authorship network for a set of Network Science researchers
+	- nodes are researchers
+	- researchers are connected if they published a paper together
+
+![[Pasted image 20240519060855.png]]
+
+- Characters from GoT: A Storm of Swords
+	- nodes are connected if the 2 characters interacted
+	- the weight of the edge represents the length of that interaction
+	- 2 different centrality measurements are present in this diagram
+		- the size of a node refers to PageRank score
+		- the size of a node's label refers to the node's "betweenness. The betweenness of a node v relates to the number of shortest paths that traverse node v, considering the shortest paths across all node pairs.
+	- both centrality metrics show that Jon and Tyrion are the most "central" characters, with Daenerys, Robb, and Sansa following
+	- This diagram drives home why GRRM will never finish his novel.
+
+## Communities (Modules) in Networks
+![[Pasted image 20240519061622.png]]
+
+- communities are clusters of highly interconnected nodes
+- the density of connections between nodes of the same community are much higher than the density of connections between communities
+
+> Returning to the previous Game of Thrones visualization, each color represents a different community – with a total of 7 communities of different sizes.
+
+Later in the course, we'll discuss nodes which can be identified as being part of 2 communities.
+
+## Dynamics of Networks
+![[Pasted image 20240519121304.png]]
+
+- systems that change over time through natural evolution, growth, or other dynamic rewiring processes
+- Dynamic Processes on Networks
+	- there is a dynamic process that is gradually unfolding on that network
+	- the network structure remains the same
+	- ex: an epidemic that spreads through an underlying social network
+
+## Influence and Cascade Phenomena
+- "information contagion"
+	- Facebook
+	- Twitter
+- not all dynamic processes are physical
+	- ideas
+	- opinions
+	- social trends
+	- hypes
+
+![[Pasted image 20240519122221.png]]
+
+> The study used network science to identify the most influential spreaders of fake news as well as traditional news.
+
+> An important but still open research question is whether it is possible to develop algorithms that can identify influential spreaders of false information in real-time and block them.
+
+## Machine Learning and Network Science
+> We will also study problems at the intersection of Network Science and Machine Learning.
+
+- NS focuses on the graph models - statistical models of static or dynamic networks that can capture the important properties of real-world networks.
+- network below comes from a paper about schizophrenia https://www.nature.com/articles/npjschz201612
+	- interactions between genes associated with schizophrenia
+	- drugs that target either specific genes/proteins or protein-protein interactions
+	- ML models have been used to predict previously unknown interactions
+	- legend
+		- round green nodes: drugs
+		- square nodes: genes
+		- purple nodes: drugs in clinical trials
+
+![[l1-ml-ns.png]]
+
+## History of Network Science
+Roots of NS
+- graph theory
+- statistical mechanics
+- nonlinear dynamics
+- graph algorithms
+- statistics
+- machine learning
+- theory of complex systems
+
+NS focuses on real-world networks and their properties
+
+NS provides a general framework to study complex networks independent of the specific application domain.
+
+## Birth of Network Science
+
+- Small-World paper by Watts and Strogatz in 1998
+	- empirical study of the "six degrees of separation" phenomenon
+- Emergence of Scaling in Random Networks by Barabási and Albert in 1999
+	- Real-world networks are "scale free".
+	- The number of connections that a node has is highly skewed
+	- Some nodes are hubs
+	- number of connections that a node has follows a power law distribution
+	- networks have a "rich get richer" property
+	- referred to as "preferential attachment"
+