Day 31 of #100daysofnetworks

Books, Books, Books!

Hi everyone. I am so excited to write today’s post. I’m announcing something I should have done a long time ago on this blog. But first!

Evolution of #100daysofnetworks

The very first #100days project I did was actually #100daysofnlp. Back then, ChatGPT did not exist, and people seemed more interested in Computer Vision than Natural Language Processing, so I created the project to learn more about NLP and to popularize it as well.

And my #100days project ideas came from one person on LinkedIn having her own #30daysof___ something. I don’t remember what it was anymore, but I felt that the amount of time wouldn’t be enough for me to learn about NLP, so I created #100daysofnlp. While working on that project, I was already obsessed with networks, and networks are prevalent where language is found. The two go together. So, the next project naturally became #100daysofnetworks.

The original #100daysofnetworks was done entirely on LinkedIn, which was not a good idea. A hashtag is not enough to organize information, and posts eventually fell off of LinkedIn. But it was a useful adventure for building interest in network analysis, and it led to a book deal and me creating a company.

I wanted a way to have “pages”, for organizing other resources, such as datasets, web links, favorite books, etc. So, last year, when I launched this iteration of #100daysofnetworks, I originally did it on Blogspot. I had an ulterior motive for that, but Blogspot just wasn’t going to work out. It’s too old of a platform and there are much better alternatives. I finally settled on Substack.

Since moving my blog to Substack, I have been just writing and writing and writing, happy as a clam. But I realized that I’m missing something. I totally forgot to create a page listing my favorite books that I learned from and/or was inspired by.

Books Section Added!

It’s great that Substack emails our articles to our readers, but it is useful to visit this blog itself, as there are several different pages already created.

Today, I added a new page for books. Here is what it looks like if you visit the blog, rather than read the email:

There are a few different sections:

• Day Catalog: easier to read list of all the days (I need to update this)

• Datasets: places to get network data to play with and learn from

• Book Giveaways!: sometimes I give my book away

• Books: THE NEW SECTION! Books I learned from and/or was inspired by

I will be improving the Day Catalog at a later day, and breaking down days by the concepts taught in each, to make this a better learning resource.

What’s In the Book Section?

You can get to the book section here.

I’ve broken down the book section into two parts:

• Books I own that I’ve learned from and/or was inspired by

• Books I own that I haven’t read enough to have much of an opinion but are good

Both sections are full of good books, but I wanted to separate them this way. The first section shows the books that actually helped me in my learning adventure. The second section includes books that I have read and learned a bit from, but got distracted and moved on to other things. I need to spend more time reading them.

All books listed have my blessing. I won’t recommend books that confuse more than help.

Where Should You Start?

It doesn’t matter what you learn, you need to build a learning path or you are just swinging in the dark. Repetition builds skill, knowledge, and eventually wisdom. But if you’ve made it this far, then you have probably already given some thought into how you will learn network analysis and who you will learn from.

But when I want to learn any new topic, I do this:

1. Have a specific topic in mind (Natural Language Processing, Network Analysis, etc)

2. Find several authors who have written about the topic. Find several books.

3. Pick a few that look the best.

4. Read and apply, from each one.

This will get you familiar with a topic. After completing that plan, come up with the next plan to dig deeper.

Where Did I Start?

My path into Network Science started with a class on Coursera. I had no idea what Social Network Analysis was when I enrolled in the class. That was a life-changing class, and I learned so much and became obsessed.

Next, I read the book Linked, by Albert-László Barabási. This is a non-technical book explaining how Network Science became a thing, and how it is valuable. This is easy reading and very relaxing. It is a fun read! Then I got his actual textbook “Network Science”, and I still pick at that book all the time. There is so much to learn.

Then I read Complex Network Analysis by Dmitry Zinoviev, and his book made this approachable by me, as a programmer. This bridged the gap from the social sciences and mathematics back to coding, my happy place.

Then I read the books about Social Network Analysis and Egocentric Network Analysis. These books introduced me to useful techniques for actually dissecting networks and investigating the parts, exploration. Graphs become much easier to work with once you lean to analyze them in pieces.

And all the while, I was reading and applying my learning to my own work, finding ways that these skills could help me in my work.

Learning Platform, Not Teacher

My goal for #100daysofnetworks is for it to become a learning platform, not for me to be your teacher. I enjoy sharing useful knowledge, but I am not doing this to teach or to make money, I am doing this to share and hopefully inspire. This is useful to me, and I think it can be useful to you, if you learn and do the work.

But my goal is not to become your teacher, or to become any kind of name in this field. I wrote a book and crossed that off my bucket list. I will write more. But I do it because I enjoy the act of writing, and I enjoy sharing knowledge, and because I love that sudden spark that people have when they suddenly “get it”.

I want you to learn, but I don’t care if you learn from me, from my book, from others, or from their books. This is important and useful skill, and I want to get the word out. But I am happy to hear from people who have read my book. I’m glad I managed to write a book.

Now I am Unstuck!

I’ve actually been a little bit stuck on this blog for a bit. I have been in a reading mood, lately, and when I am in a reading mood, I am busy learning, not focused on teaching. Learning takes time.

I’ve been going back to fundamentals, reading Network Science, revisiting concepts. I haven’t been experimenting as much as I have been just reading and thinking. I’ve had more to think about and less to write about.

But now, on some days, I might just write about what I’m reading, instead of making a post explaining a concept. I, myself, will use the new books section to remind myself of what books I need to revisit (the second section).

Reading is fun, and talking about what I am reading is also fun.

That’s All for Today

Thanks for reading. People often ask me what books I recommend, so I’m happy to have this added. I will be sure to maintain it. Please do recommend your favorite Network Analysis in the comments sections.

Thanks to everyone who has been following along with this series. Happy learning! If you would like to learn more about networks and network analysis, please buy a copy of my book!