Blog IV | Module-3 | Social Media Analytics, Data Visualization and Network Analysis

This blog post is for Module 3 (Social Media Analytics, Data Visualization and Network Analysis). It will discuss the different lectures and reading materials that fall under this module.

The blog will cover the below topics:

1. Introduction to Networks
2. Introduction to Network Visualization
3. Network Properties

Lecture Summary:

In the "Lecture 11: Introduction to Networks" lecture I learned that Network is a collection of entities with relationships among them. Everything around the world is about interactions. To understand a complex system, networks are very useful. A network is a social structure made up of entities also known as vertices and relationships between entities. A network can also be called graph, a graph can then be a set of vertices and a set of edges. Additionally, a network can be represented using two components: nodes are vertices and edges are links. The vertices in a network represent entities in a system. Edges present relationships between nodes and vertices. A network can have different types such as a single mode networks and two mode networks.

In the "Lecture 12: Introduction to Network Visualization" lecture I learned that information visualization form a mental image of a concept, idea or a object to make perceptible to the mind or imagination. The purpose of information visualization is to explore datasets and can be used to communicate ideas that are from data, the data can be understood. There are different layouts of network, such as force directed layout, geographic layout and hierarchical layout.

In the "Lecture 13: Network Properties" I learned that degree of centrality is simple but very powerful. It has undirected network and directed network. The undirected network there are only one kind of degree centrality measure. In the directed network there are two kinds of degree centrality measures. Additionally, a path between any two nodes is a sequence of a non-repeating nodes that connects the two nodes. Also Betweeness Centrality is calculated for every node, it is the number of shortest paths that pass through a node divided by all the shortest paths in the network. It is also used to decide which nodes are likely to be in the discussion between other nodes. It can also show whether a network would break if a node disappeared.

Reading Analysis:

The "Thinking in Network Terms" reading discussed how the internet brought connectedness, It connected information using technologies and then eventually using Google, Facebook. We started to see a lot of data and started to collect that data from this these networks, so many changes happened using data. It also discussed how data is being more and more accurate, that data is all around us and every field is impacted by data. We are currently living in the age of networks, it is important to understand how these networks function. The reading also discussed how we are currently living in an economy that is the economy of information, of interconnectedness.  We now leave a trail of information around because we now have devices that track every single thing that we do. We should also assume that there will be nothing private in the future. More information is being recorded and collected. The future of us is heading to a place where nothing will be private. Data, and how we handle data is the gold mine for science these days and will be in the future as well. 

The "Explore Your LinkedIn Network Visually in Maps" reading discussed how LinkedIn network visualization looks. LinkedIn has opened up a InMap to visualize peoples links as a network diagram. InMaps is an interactive visual representation of individuals professional world. It lets people understand the relationships between themselves and those that they connect with. Someone in the article mentioned that they would like to see how their segment their networks and identify those who cross the boundaries. People found so many things about themselves and their connecters by exploring their maps. 

The "Social Network Analysis" reading discussed how social network is made of people who are connected to other people. Social network has emerged as a key technique in modern sociology. Smaller networks are not as useful to people than networks with lots of loose connections to people outside the main network. Additionally, visual demonstration of social networks are very important to understand network data and to show the results of the analysis. The data is shown through displaying nodes and ties in various layouts and adding colors. 

The "Networks, Crowds, and Markets: Reasoning About a Highly Connected World" reading discussed two main theories: graph theory and game theory. Graph theory is the study of network structure, while game theory provides models of individual behavior in settings where outcomes depend on the behavior of others. Graph theory introduced two new terms. Strong ties, which shows close and frequent social contacts and weak ties, showing more casual and distinct social contacts. In game theory it was observed that there are a lot of places in which a group of individuals must simultaneously choose how to act, knowing that the outcome will depend on the joint decisions made by all of them.

Personal Opinion:

I really enjoyed this weeks materials. I did not much about "networks" I had no idea how they worked and what exactly a network is. I appreciated how detailed the lectures were in explaining what exactly it is and how it works. It was still challenging for me to grasp this weeks material, mostly because this material is so new to me, but I am happy that I know more now then I did before this week. My favorite reading was definitely "Explore Your LinkedIn Network Visually in Maps" because I have a LinkedIn account and I did not know that they offered this tool. It was interesting to read what others thought of the InMap, and how they used it. I have to explore the tool myself since I am pretty active on LinkedIn. Overall, this week taught me something new and I am happy that now I can apply the material and knowledge to my professional life. 

Additional Materials:

If you are interested in LinkedIn InMaps the way I am, please read the material below. The article discusses what the map is, what it does and how you can get one. I know I am very interested in seeing my InMap. 

Reference:

Uwe MueggeFollowThought leader in terminology-driven translation management. Translation is an exercise in efficiency!™Like16Comment26ShareLinkedInFacebookTwitter0, et al. “Get Your LinkedIn InMap While You Still Can!” LinkedIn, www.linkedin.com/pulse/20140813003941-3987842-get-your-linkedin-inmap-while-you-still-can/.