Day 28 of #100daysofnetworks

Pause and Reflect; Retrospective; Where are we going?

Hello everyone. The last several posts have been quite technical, and life has been very busy, so today I want to just pause and reflect. I want to do a few things: I want to think about what we have covered and what we will cover in the future. I want to write about how I feel about the progress of this blog and my attempts to popularize Network Science. I want to talk about some of the nice improvements I’ll be doing in coming days. And finally, I want to get your thoughts on Influence Theory swag.

I’ll Start with Swag

I’m going to start at the end and do those out of order. I don’t spend much time thinking about swag (t-shirts, stickers, etc), but swag is my favorite part about going to conferences. However, there’s a few things to know about me:

1. I have no intention to make this into a business. I already have a company, and that is my top priority. This is a very, very distant priority, and more of a creative outlet for me. This is art, for me.

2. I don’t need money. I wrote the book with no intention of making money. I wanted to fill a knowledge gap, and that’s the same reason for this blog. I want to write about something useful that is not getting the attention it deserves. None of this is for money. I am utterly apathetic about getting money out of this.

3. I don’t have a lot of free time. I use some of my weekend free time on this, just because it is a creative outlet to me. Playing with networks and improving my skill is useful to me. This is really win win, and it is equally, selfishly for my own good. I am an extremely creative person, and this is a necessary creative outlet for me.

With that in mind, I do still sometimes think swag would be cool. My own substack is named “Influence Theory” and I really like that. I occasionally also come up with goofy ideas for tech shirts, like Failure Driven Development instead of Test Driven Development. Is there any interest in swag? I know I’d wear Influence Theory stuff everywhere, so maybe I’ll just do it for me unless others are interested.

Ok, moving on.

Where Have We Been?

On day 1, I discussed many of my ideas for the things I would demonstrate. Looking at the list, we’ve covered everything. We’ve at least scratched the surface. In my opinion, we are well past the basics and are doing interesting things.

I have created new techniques for both Temporal Network Analysis as well as Community Detection. New techniques lead to new ideas, and I am going to take both of these further, as far as I can go in 100 days. And if this is anything like the first iteration of #100daysofnetworks, it will probably go forever.

I have also shown how you can use Wikipedia, arXiv, and Spotify to create your own interesting networks that are useful to you, not just how to download .csv files from Kaggle or other open data repositories. I’ve shown you how to chase curiosity and study what you find.

And I’ve shown you how to do storytelling after network analysis. You can use me as an example when you do your own network analysis and have to report your findings to those in your organization.

Where Are We Going?

We’re going to get network data from more internet sources, such as Youtube, just like was done with Spotify, arXiv, and Wikipedia. The internet is an amazing dataset if you learn how to use it as such, and you can learn from it. I’m sure I wont stop with Youtube. There are other sources we can find. I show this to show you how you can study the things you are interested in. The source matters less.

I am also planning to get really experimental with Temporal Network Analysis. I think I will do that next, as the last three posts have been about Community Detection. I have a book that I am really wanting to dive into about Time Series Analysis, and I’m going to fuse Time Series Analysis with Network Science and show you how. Temporal Analysis is Time Series Analysis of Networks.

I plan to do more experimentation with both Community Detection and Egocentric Network Analysis, as well, and still have a dream to create an approach for Exploratory Data Analysis (EDA) of graphs.

What do you want to learn about? Post in the comments or let me know on LinkedIn. You all are really quiet, but I enjoy the enthusiasm when I talk to readers one-on-one. Some of you are really understanding the opportunities this exploration presents.

How Do I Feel?

I restarted this challenge for a few reasons. First, I really needed a creative outlet. I wrote a book last year, and I want to keep that going forever. This series helps me have the discipline to practice and build techniques that I want to go into the next edition of my book. Second, I wanted to popularize this a bit, but with the knowledge that this will never be anywhere near as popular as the hype of the day. I don’t mind that at all. The intention has never been popularity. I wanted steady growth, and I wanted to impact a few people’s lives, and give the ability to do a little of what I can do.

So, when I look at the stats, and I see that there is steady growth with subscribers, that makes me happy. I wanted to popularize this topic a bit, and I’m doing it. If I compare myself for blogs about LLMs or ChatGPT, I’d feel pathetic about having 200 followers, but my goal isn’t to add more hype to hype. My goal is to teach something incredibly useful that is incredibly overlooked.

Likewise, when I look at daily stats and it seems that there is only a little more interest today than a week ago, that doesn’t bother me. I don’t care if this becomes wildly popular. I want to help a few people, and a few people can change the world. Network thinking has already changed the world (Google) and continues to change the world. So, I just smile and nod, even if growth is slow. This is a marathon, not a sprint. Influence takes time.

How Is This Useful to Me?

As I have said before, these approaches give me the ability to explore my reality. These techniques help me get to the root of problems. These techniques help me understand how and why things are happening. And these techniques help me cut through noise and find value.

For instance, the last few days posts have been an exploration of an Artificial Life dataset, that also contains interesting Artificial Intelligence content. Above is one of thousands of communities in the dataset.

When I find a community that writes interesting content, I “follow” them, like you would on social media. I look into what other content they produce that can be useful to me.

I found these research papers while doing the analysis that went into yesterday’s post, and I have been reading them. These are the kinds of papers I enjoy, not yet another paper on the same thing others have written. Conversation Killers, Deception Detection, Malware Detection using Artificial Life, Natural Backdoors in NLP Models, and so on. Wicked cool!

These skills help me find things. These skills help me learn about the content, but also about the ecosystem (the authors and communities) that produce the content.

That’s how it has been useful to me in just the last 24 hours. I have already written other posts about how this has been useful to me, in this series.

I Feel Very Good and Thankful

Having said all that, I feel very good about everything. This is the creative outlet I needed, I have enhanced a few people’s lives with my book and these posts, and I am learning new things. I don’t mind the slow growth. I expected it. I knew that my book would be the same. I don’t need or want to be a celebrity scientist. I just want to teach stuff that is useful.

Thank you all who have followed so far. This is only going to get better over time.

Oh, and hey, I’ve got some books to give away. I’ll try to do another giveaway soon.

That’s All, Folks!

That’s all for today! Thanks for reading! If you would like to learn more about networks and network analysis, please buy a copy of my book!