This blog has moved to Substack! No more updates will be added to the blogspot blog. I will leave posts here but will not add new ones.
New Address: https://100daysofnetworks.substack.com
Thanks, everyone who has followed along so far. It is only going to get better!
Welcome to day 8 of #100daysofnetworks!
If you would like to learn more about networks and network analysis, please buy a copy of my book!
So far, through the course of this adventure, we've already learned some useful things such as:
This is already more than enough to be very effective in network analysis. This is because it is useful to be an explorer of networks and graphs, not just a user of graph data. I consider these as things to explore, not just things to use. By that, I mean that graphs are more than just input data for Machine Learning models, and that whole network metrics (overall density, number of nodes, number of edges, number of triangles) is just really not all that useful, in reality. When I do network analysis, I am looking for things, and it is never just a count of edges, or count of triangles, or a count of anything.
With the above skills, we already know enough to get started exploring networks, so let's get away from the dry fact learning, and do something interesting. Let's use what we know and use it for MUSIC DISCOVERY! This is an excellent weekend activity.
I have one goal: I want to learn three to five new things about my favorite band. I specifically set that to three to five, because it is easily possible to spend hours and hours and hours digging through a network. You should set goals so that you have something to aim for, and so that enough is enough. You can always return to an interesting network.
Let's get to work!
My favorite band is Wilco. I have been listening to them since 1997. I love their folk/rock sound and their experimental nature. They aren't just folk music. They get almost jam band'ish at times. They play in chaos. But they are also really mellow, and I love their folk and rock roots. I play guitar, and I find their music approachable. I am insecure about my ability to play lead guitar, and their lead guitarists has a style and skill that is approachable, where other artists are much more difficult.
Here's a picture of Wilco I found on a Google search.
In the front is Jeff Tweedy, the singer and songwriter. He is easily my favorite songwriter and frontman.
Here's one of my favorite Wilco songs.
They're just a great band, and I love to listen to them when I need to relax. I love playing guitar along with them as well.
You can learn more about them here.
Yesterday, I used my Wikipedia crawler to create an edgelist for exploring the Wikipedia network that exists around Wilco. Let's get to it!
As always, the code is available on Github.
Datasets are also available. Use these to explore if you don't want to use the crawler!
This time, the dataset was a little noisy/messy, so I used Wilco's "ego network" with a radius of 2 to create a purer Wilco graph. You can see how I did that in the code. I do not explain to do that in my book. It is a useful option for quick cleanup.
After creating the network graph, the first thing I usually do is inspect the core of the overall network.
I can see that there are two parts to this core: there's the actual Wilco stuff on the top left, and there's a bunch of stuff related to Roger Wilco on the bottom right. Because of the name, Roger Wilco was pulled into the dataset while crawling, using "snowball sampling". When using snowball sampling, you're always going to get some stuff you didn't expect, and it can be seen as junk or as something interesting. To me, this is interesting, not junk.
Notice that it looks like this core would fly apart if someone took a pair of scissors and made a few snips in the middle. If we were to cut four edges, the network would cleanly split in half. We're going to actually explore network resilience soon and will play with that idea.
I look at the core of every network to understand what is influencing the network. The core are nodes that are most connected in the network. The core is the foundation, and it can say a lot about the overall makeup of the network.
But the core is not everything. Isolates (nodes with no edges) can be very important, too.
However, the core gives me an overall picture of what is driving this network.
After looking at the core of a network, I want to understand which are the most important nodes in the network. This can easily be done using Page Rank or one of many of the different kinds of centrality measures that we discussed previously, such as Betweenness Centrality.
This is a small network of only a few hundred nodes, so I will use Betweenness Centrality (which is slow on massive networks) and Page Rank to identify important nodes based on their network positioning.
First, here are the nodes that Page Rank found to be most important.
With my goal of finding 3-5 new things about Wilco, I'm going to look closer and see if anything stands out, and turn it into a question:
Now we are entering the realm of open source intelligence (OSINT)! Let's find out who each of them is and how they relate to Wilco!
Other than Billy Brag, I did not know about the rest of them! I struggle to memorize names, and I haven't kept up with the individual musicians in Wilco. So, already, I've identified two bands that might be cool to listen to (Uncle Tupelo and Tweedy) and found musicians that might be worth further exploration (Jay Farrar and John Stirratt). The goal of finding 3-5 new things is already met, and we are just getting started. That's real value, and amazingly quick insights.
Next, I use Community Detection, to see how Wikipedia pages link together to form network communities. I prefer the Louvain Method, but Karate Club has a Machine Learning model called Scalable Community Detection (SCD) that is also excellent. I show how to use that in my book, and we will use it later, in this #100daysofnetworks adventure.
Community Detection essentially separates a graph into a bunch of smaller graphs that can be explored separately. It is useful on massive graphs, to break them apart for separate analysis, and it is useful on small graphs as well, to detect the cliques and communities that exist.
In the code, you can see more communities, but I will show and talk through just a few, here.
It makes sense that this is the largest community in the network, as this is one of their older albums. The album itself is a beautiful tribute to Woodie Guthrie.
Let's look at another community.
I'm not going to chase those answers down right now. Let's keep going! That gives me something to explore when I need new music!
Let's look at another community!
Notice something. Everything that we explore in a network brings up more questions. Graph exploration can unveil a MOUNTAIN of insights. Keep a notebook next to you, or a way to keep notes on your computer. Networks are complex and gradually an overwhelming feeling builds up if you explore too long without taking breaks. Look around, write notes, look around more, write more notes, then walk away for a while and take a long break. Good network analysis isn't done in one shot. It is iterative. Find insights, ask questions, do more digging, repeat. Do this until you have as many answers as you need. There is no DONE. You are done when you have enough.
Let's jump to Ego Networks, as I had the most success working with them for this analysis, more than communities. Check out the code to see more communities!
With community detection, we were interested in how pages linked together to form a community of nodes.
With Egocentric Network Analysis, we are interested in seeing what nodes exist around a node of interest, and how they link to each other. The main node is called the 'ego node', and all other nodes are called 'alters' or 'alter nodes'.
In an "ego network", the node of interest will typically be in the center, and alter nodes will surround it. However, one of my personal techniques that I find useful is to DROP the center node, the ego node. By doing this, the ego network can split apart, revealing the distinct groups that exist in an ego network. For instance, if a person is both an athlete and a video gamer, their social circle might contain both groups, and they may not interconnect. That's one example.
Opportunity for intuition: what other combinations could cause a person's ego network to have different groups? Think hobbies, religions, dating life, education, etc. Humans are complicated.
Let's look some ego networks!
And that's enough for one day! It is important to pace yourself when you do this kind of analysis.
My goal was to identify three to five new things I did not know about Wilco and I found many more than that.
What are the takeaways?
This was a fun post! Next post, I'll have some cool music to listen to while I am doing the work! I hope you enjoyed this!
And for those who took the time to read this post, I am now going to announce a book giveaway! I am going to give away five copies of my book in digital and physical format.
Hew is how to participate:
You don't have to be too thorough. Just do what you can. If you know nothing, then building a graph and finding important nodes is enough! If you know your way around networks, then push a bit further and show me what you can do!
I will accept submissions until September 30, 2023!
I will take all acceptable submissions and add them to a raffle. I will call out the winners on October 1!
And if you make your own LinkedIn post and tag #100daysofnetworks, I will add your name TWICE!
If a submission is not complete enough, I will ask for a bit more. I will try not to do that. Do the work, and you'll have a shot to win. Do the work.
Thank you for reading this post! I hope you enjoyed it! I wanted to do something a bit more interesting than usual, as it is important to do things that keep us creatively engaged. I look forward to seeing what you can do!
If you would like to learn more about networks and network analysis, please buy a copy of my book!
If you would like to learn more about networks and network analysis, please buy a copy of my book!
In Day 6 of 100 days of 100daysofnetworks, we converted our Network Analysis graph into an actual study guide we could use to learn more about networks and graphs!
Today, I chose five types of graphs to explore from the study guide. You can see the study guide here. More specifically, today we are going to look at these five types of graphs:
I recommend that you follow along with the code, today, as it explains how I did this and what I see. This blog post will be kept high level. Follow the code!
We're not just going to LOOK at these kinds of graphs. I will describe their characteristics, and if you look at the Jupyter notebook, you can see how they differ from each other in terms of centralities and shortest paths.
Network Analysis is exciting, and it's good to learn about different types of graphs, so that we have a name for them when we see them in the wild. Let's begin.
I will use summaries from ChatGPT. As I mention in the notebook, if you use an LLM, you should validate the output. Let me know if you see any errors in the summaries. They look good to me.
Finally, we have not discussed density much in regards to graphs. Density has to do with the interconnectedness of a network/graph. If every node is connected to every other node, the network will have a density value of 1.0. If zero nodes are connected with any other nodes, the network will have a density value of 0.0. Density tells a lot about how connected the network is.
ChatGPT: "A cycle graph is a type of graph in network science that forms a simple cycle, which is a closed path where each node is connected to exactly two neighbors except for the first and last nodes, which are connected to each other. Cycle graphs are fundamental structures in graph theory and are often used to model cyclic processes, circuits, and other scenarios with repeating patterns.
In other words, in a cycle graph, nodes are connected to a single node to form a cycle.
For example: A -> B -> C -> D -> A
Here is the graph I built.
The structure of the graph also affects its centrality scores, as well. Every single node in this network has a betweenness centrality value of 0.5. No node stands out as being more important than any other node.
Shortest paths will be affected by the structure of this graph, as well:
Shortest paths between nodes are not equal. This is not ideal for information flow.
If you wanted to make this more optimal for the flow of information between nodes, what do you think would help?
ChatGPT: "A dense graph is a type of graph in network science where most of the possible edges are present, resulting in a high density of connections between nodes. In a dense graph, the number of edges is close to the maximum possible for the given number of nodes. Dense graphs are often used to model scenarios where interactions or relationships between entities are widespread and frequent."
In other words, in a dense graph, most nodes are connected with most other nodes. Here is the graph I built:
The structure will also affect centrality scores. Check the notebook/code. Every node has a betweenness centrality of 0.0 and a degree centrality of 1.0. Essentially, all nodes are equally important, structurally.
However, compared to the cycle graph, this graph is optimal for information flow. The shortest path between each node will always be equal, because every node is connected to all other nodes. The shortest path from A to C is simply A->C, and the shortest path from E to C is simply E->C.
If the above is a dense graph, what do you think a sparse graph is? That's right. It is a graph where most nodes are not connected with all other nodes. It's the opposite of a dense graph. Here is an example:
Notice a few things:
This is a sparsely connected graph, or a sparse graph. It will have a very low density score. This graph has a density value of 0.027. The dense graph had a density value of 1.0. In the wild, you will mainly run into sparse networks, and that is not a problem. That is the nature of reality. Networks follow Power Law, or the idea that Rich Get Richer. A few nodes will have many edges, and most nodes will have few edges. A few humans will be billionaires, and most people will not. Etc.
Betweenness centrality can be very useful in a sparse network, as can Page Rank and other centralities. If you look at the notebook, notice the following:
Shortest paths between nodes are also impacted by the structure of this graph. Scroll up and think about this: how would node E talk to node K?
The answer is that it cannot. No information flow can take place between nodes E and K, or E and any other node, because node E is an isolate. It has no edges.
How would node A communicate with node D? It would go A->B->D.
The structure of a network has real world impact in how freely information will move in that ecosystem. Ideas are shared in a dense ecosystem. Information is essentially firewalled in isolates. Nothing is shared from or shared to isolates.
ChatGPT: "A regular graph is a type of graph in network science where each node has the same number of connections or edges. In other words, all nodes in a regular graph have the same degree. Regular graphs are often studied for their uniform connectivity patterns and symmetry."
In other words, a regular graph is a graph where all nodes have the same number of edges. Here is my example:
So, there will be a few things to keep in mind:
This is simple enough. Let's keep moving.
I had never heard of or played with a Wheel Graph before today, so this was fun for me.
A wheel graph is identical to a cycle graph, but each of the outer nodes links to an inner node, creating what looks like a wheel. Here is my example:
In terms of centralities:
In terms of shortest paths, check out this image:
The shortest path between any node in this network is never more than three steps, because node H exists as a shortcut. To get from node A to node E, we can jump through H to get to our destination. Without H, we should have to go A->B->C->D->E.
I think of this as the structure of a graph, or the structure of a network. Different networks behave differently, and I hope I showed that today.
There are real-world implications to this:
Thank you for following along! This was fun! We've only done five of the graph types identified on day 6, and it was quite educational already!
If you would like to learn more about networks and network analysis, please buy a copy of my book!
If you would like to learn more about networks and network analysis, please buy a copy of my book!
In Day 5 of 100daysofnetworks, I created a Wikipedia crawler that could be used to investigate any topic that is of interest to you. You should really use it. It is a lot of fun to use and creates useful network data.
What do I mean by useful?
Well, today, I'm going to literally convert a graph into a learning curriculum for myself. I am going to take this:
And turn it into this:
True story. When I started working with networks, I got a lot of push back for using them as people could not understand how they could be useful. There is too much information in one visualization, and the more complex the network, the more difficult it is to visualize. Look at this mess (scroll up). How are we supposed to pull anything useful out of that ball of yarn?
It is not difficult if you know how. My book shows how, and this series has shown how as well. In the previous days, I've already shown a bit of how we can zoom in on parts of networks. For today's work, we are going to be zooming in on communities, but our final approach involves essentially "peeling the onion" layer by layer. I will show how. This is a cool technique, and not shown often. I will get to that after the Community Detection Work.
One thing that I have found to be very useful is to look at content from a community perspective. Like attracts like. Pages that are related will link to each other, forming clusters, or communities.
You can see today's code here.
Check out the code to learn how to do the Community Detection work. I'm only going to write about what I see in blog posts, not describe code.
What other pages are part of this community? Let's see a list of only pages that are in this community.
Super easy. just like that, we can see the nodes that are part of any community, and in this case, this community is Wikipedia pages related to Network Science and Social Network Analysis. This is a clean list of relevant pages.
Here is another community:
Let's look at another:
This would be a pretty cool starting point for learning about Neural Networks. Let's look at another network.
There are many more communities in the data, with a network this large. I encourage you to play with the data and to explore the networks. You will not learn this without practice. With practice, it becomes easy and intuitive.
I have shown that we can look at Wikipedia data not only from a whole network perspective but also from a community perspective, and the community level is a good way to hook onto the signal that you want. That's how I think of it, at least. Networks and data can be used together. They do not need to be separate things. They are powerful when used together.
I use two techniques for exploring the layers of a network:
Using K_core, I can see that the maximum depth I can go is six edges.
If I go past six, the code will fail. So, six is the number to keep in mind. Now that we have identified this using K_core, we will use the values [6, 5, 4, 3, 2, 1, 0] in K_corona to see all nodes that have exactly six edges, then exactly five edges, then exactly four edges, then exactly three edges, and so on.
Why?
Because I can use this to build a study guide. The most connected nodes are more important than the least connected nodes, in terms of learning. The most connected nodes have to do with other nodes, and understanding how things relate allows us to build a strong foundation. Studying fringe material is less useful, unless you are solving some fringe problem that it relates to. There is a time to focus, and a time to zoom out.
In the Jupyter notebook, I show simple code to convert the main Network Science community into a study guide. Please see that code.
As a result, I now have a clean list of stuff to explore and learn more about.
And so on. Network Science is all about RELATIONSHIPS between THINGS. Networks are essentially things and their relationships.
This madness:
Finally, if you are interested, I threw the study guide into a document and made it available for everyone. You can access it here.
My goal for today's post was to make and show something useful. Unless you have a reason to study networks, there never feels like a reason to study networks. Same goes for any topic. But here is the thing: networks are all around us, and network data is easily accessible, even if you have to generate it yourself. If you are creative, you can use this to your advantage. You don't have to use everything for work.
I have used networks to create vocabulary lists, building a network of Jane Austin's word use, and then extracting nodes with only a single edge (words only used once).
I have used networks to understand the flow of ideas across space and time.
I have used networks to troubleshoot server problems in minutes that used to take days.
I have used networks to study how malware evolves, and to use that to detect undetected malware.
And on and on and on and on and on.
I think in networks, because life is networks. Life is not spreadsheets. Life is not lists. Life is people and things and relationships.
So, I encourage you to learn to study networks, and this is a good place to start, as is my book, and I will recommend many other books during the course of this adventure.
In the Jupyter Notebook, I also included some code for working with the Wikipedia Python library. I show how to use it to pull summaries and text for any of the nodes in the Graph. I thought that might be useful to spark creativity.
Thank you for following along on this adventure! If you would like to learn more about networks and network analysis, please buy a copy of my book!
Welcome to day 5 of 100 Days of Networks!
If you would like to learn more about networks and network analysis, please buy a copy of my book!
Today, I have a special treat for you! I created a Wikipedia Edgelist Generator that you can use for knowledge discovery on any topic that interests you. You can find the crawler / edgelist generator here!
I wanted to build a tool for knowledge discovery, and something that could be used no matter the topic to create complex networks that are more interesting than the small networks that come with NetworkX, and more interesting than using somebody else's dataset. To me, there is nothing more interesting than my own research, and I don't like using other people's datasets for learning. I prefer to analyze my own constructed networks, and do two things at the same time:
Today, I created the edgelist generator, and you may use it. I have added some guidance regarding iterations and sleeps at the top of the notebook. Please be responsible or Wikipedia will block your IP and you will get nothing.
The point of the edgelist generator is knowledge discovery, on any topic. For instance, to build today's dataset, I searched for four things:
If you look at the code on github, you will be able to see where and how I did that. After four iterations/loops of the crawling and edgelist generation, those four searches built a network of OVER 9000!!! nodes. That is why I call this knowledge discovery. Each of those 9000+ nodes is a Wikipedia page on a related topic. You will understand this more if you continue reading below.
If I had done five iterations instead of four, I might have ended up with 50,000 nodes or more, which is more than I wanted for this dataset, and would query Wikipedia's API harder than I wanted to do. You should start with two iterations, check your results, and then increase RESPONSIBLY.
I created a second notebook (which is nearly a duplicate of Day 4 but with today's data) for analyzing this network data. You can see the code/notebook here!
Previously, we used the Les Miserables network to learn a few fundamentals. From now on, we will use Wikipedia networks, as they are complex and more meaningful than character names. We can literally use the node names to continue our research into any topic of interest.
Today's created network is far more complex than Les Miserables.
For today's update, the edgelist generator is the most important thing, as it is useful and we will use it to create interesting datasets during the course of this adventure. I have other topics in mind that I would like to understand.
Today, I chose the four 'seed searches' because they are all related:
I was especially interested to see the overlap between Causal Inference and the rest, and I will explore that in later days.
The generator itself requires some understanding, so I will keep the analysis light today. We will just look at a few ego networks, and discuss what we can see and do with the information.
It is always useful to look at Page Rank and centralities, to identify important nodes. That is always a good place to start after a graph has been constructed.
This is a complex ego network! There is a lot of connectivity between the alter nodes, and this is not at all a star network! This is a complex ecosystem of information having to do with Graph Theory. But this is hard to read. Check out the Jupyter notebook and you will see how to get a list of nodes.
What nodes have we uncovered? What interesting Wikipedia pages and topics have we found? Let's take a look. Here are just the first twenty nodes out of seventy-seven:
Very cool. I can already see several things I have never heard of. This could lead me down some interesting rabbit holes of discovery and education. Each of these is a separate Wikipedia page, and you can also search other sources on the internet, such as Arxiv. Let's look at more interesting ego networks.
This (above) is the ego network for "Graph Theory", one of the original searches. There's lots of interesting topics, and even a page relating to Graph Databases.
I've long said that the internet is a goldmine for discovery and analysis, if you learn how to use it as such. That is the reason for my obsessions in Natural Language Processing and Network Science. Natural Language Processing gives me answers regarding content and context. Network Science helps me understand relationships and flow.
What I've demonstrated today can be a very useful tool for anyone to use for learning. You don't have to use my seed search terms. You can use your own. You could research any topic at all that interests you. For instance, I will use this to build a network relating to some of my favorite scientists and science fiction authors.
I want to encourage you to JUMP IN and try this stuff out. It feels good to create your own networks and do your own network analysis. You can share edgelists with the community, and you can discover insights that you would likely not discover, otherwise.
And now, we have a tool that we can use to make this #100daysofnetworks adventure a lot more interesting, beyond using stale NetworkX network generators (Les Miserables, etc) or other people's datasets. Research what interests you, and then use that data to build skill. Then the skill sticks, and you learn neat things in the process.
If you would like to learn more about networks and network analysis, please buy a copy of my book!
Welcome to day 4 of #100days of networks.
If you would like to learn more about networks and network analysis, please buy a copy of my book!
You can find the code for today's exercise here.
Today, I am going to show you how to ZOOM IN on any part of a network. We've made good progress on this adventure, so far, and we're following a logical path.
Why would we want to ZOOM IN on important nodes? Well, there are different ways to look at any network:
But today's discussion is on Egocentric Network Analysis. We are going to ZOOM IN on nodes of interest. That is the simplest way to think about Egocentric Network Analysis. It is less complicated than it sounds.
ALWAYS, it is a good idea to start any network analysis by doing Whole Network Analysis. However, we've looked at this network many times and know that it is small and simple enough to visualize, so let's do that, and use our eyes for insights.
Next, I am going to show you how to "zoom in" on any node in the network. Scroll up and try to identify all of Claquesous's connections. It's very difficult to do, because he is part of a denser area in the network. For this, we need to be able to look closer.
The best first option for looking closer is to look at a node's Ego Graph. In an Ego Graph, the node of interest (Claquesous) is in the center, known as the ego node. All of the node's connections are shown as connections around the ego node, and they are known as alters. The two things to keep in mind: ego and alters. The ego is in the center, the alters are around it.
A very cool thing that can happen in an Ego Graph is that you will also be able to see alters' connections to other alters. Rather than an Ego Graph simply being a star, sometimes there are other connections that can be interesting. In those cases, you can look closer with your eyes, or you can take another approach, which I do often: drop the center, and the alters will show as isolates and small groups.
I will attempt to show all of this in this notebook. First, let's use PageRank to identify the most important nodes in this network.
Now, let's look through the top five characters shown in the above visualization.
Here is Valjean's Ego Graph.
When an Ego Graph has plenty of complexity and is interesting to look at, one of my favorite tricks is to DROP the center. By doing this, it drops the ego node (Valjean) out of the graph, causing the graph to shatter into pieces, exposing the groups that exist in the graph. Let's do that.
But most importantly, we've identified that Valjean is connected to two separate groups. The differences between these groups could make for interesting analysis. Why are they not connected? What do they do differently? And why are none of the isolates connected to anything else? What makes the isolates so utterly unspecial or special that nothing is connected to them?
Let's keep moving. I am going to do the same for the next four important characters. We could do this for every single node in the network, and it would take a very long time to analyze, but a tremendous amount of learning could be done about the story of Les Miserables if network analysis was used along with content analysis to dig deeper. Thus, the marriage of Network Science, Social Network Analysis, and Natural Language Processing is special and important to me. Moving on.
For these next characters, put your thinking caps on. Look at the images and try to answer the questions I ask.
Like Valjean, Gavroche has a very interesting Ego Graph. I can see the one center node. How many groups do you see? A group can be two people. If we drop the center node, how many groups do you think we'll be able to see? How many isolates?
Did you guess the number of groups correctly? How about the number of isolates?
One of the groups was Child 1 and Child 2. What is their relationship with Gavroche?
What is the isolate's relationship with Gavroche?
Finally, what is this larger cluster of characters? Why are two groups linked together, with or without Gavroche? Who are these people? How would the absence of Gavroche in the story affect these characters?
Marius' Ego Graph has some interesting complexity as well. I can see at least one densely connected group of nodes on the top left, and I get the feeling that this is actually two separate groups of people but that there is some cohesion with the top left group. I expect that this network will not shatter if we drop the center node and that there will be no isolates. What is your bet? Try to draw a mental picture of what will happen after Marius is dropped.
As I expected, the group remained intact, even with Marius removed. Valjean is an important node in this network, and he has helped keep it together, along with others.
Who are these people? How do they know each other? Why is this network so resilient? If these characters are important, what would it take to eliminate their ability to work together? On the other hand, what would bolster the network?
What do you see? I see to central nodes: Javert and Valjean. I see one group of nodes on the left, and they are connected with both Javert and Valjean. I see some characters on the right who have connections to characters on the left. Because of all of this, if the center node is dropped, I suspect that this network will be resilient and not shatter. Because none of the nodes have fewer than two edges, I expect we will have zero isolates, because 2 - 1 = 1. Every remaining node will have at least one edge to another node. The network will remain intact. Essentially, this is a large group of connected individuals.
What is this group? Why are two very important characters so central in this network?
It is fun to explore any kind of social network, no matter the topic. You can learn a lot about any topic by exploring the social networks that exist inside that topic. In today's exercise, the topic is Les Miserables. We could have taken the raw text of this story, used the techniques from my book, and literally converted raw text into an explorable network. We can use the text of the book alongside the network to learn more about individual communities, and I will show how to do this at a later date. This is new material that is not included in the first edition of my book.
This exercise also showed that different shapes of networks are more resilient to attack. For instance, if you take a star network (the second character) and drop the center node, the network shatters into pieces. If you take a more densely connected network and drop even the most important node, the network can still remain intact. What are the implications of that for cybersecurity, for leadership, for national security, for teamwork, or for your own life? What fragile networks exist in your own life? What resilient networks exist in your own life?
For instance, in my own life, I am part of the LinkedIn Data Science community, and regularly post content and participate in conversations. That is a densely connected network, and that network would not be affected by my absence, or any one person's absence. It would just continue to grow and evolve. That's a resilient network that exists in my life. How about a fragile network? I have very few friends I hang out with in person. In a small group, if one person is removed, the effect is devastating on the group.
I hope you have learned a bit from these discussions. We've already covered enough material for you to jump into network analysis. We haven't talked at all about network construction, but we've found a network to use and learn from. I promise, very soon, we are going to construct our own Graphs, not use something pre-made. I enjoy using networks to explore reality, not just use someone else's networks for learning.
We've learned how to construct a graph, render visualizations, identify important nodes, and zoom in on important nodes. These are fundamentals that you need, and you have them now, and we're only on day four. Getting the fundamentals out of the way in the beginning will leave us with a lot more time to explore.
If you find this content interesting, please jump in and give this a try! Install Jupyter or use Google Colab and start exploring. You don't need to know everything on day one. Just get started. Learning to work with networks and explore relationships is powerful, and this skill becomes tremendously useful the deeper you go.
I hope you found this to be an enjoyable read, and I hope my explanations made sense. This blog post was written quickly. If you would like to learn more about networks and network analysis, please buy a copy of my book!
