Tina Eliassi-Rad

I'm not going to be a very popular person, but that if you get taxpayer dollars from in your reports to the government, you have to have a section on assumptions and technical limitations. Because the problem is the way the peer review culture goes is that if I have a technical limitation section in my paper, the reviewer will just copy and paste it and say reject, right?

I'm not going to be a very popular person, but that if you get taxpayer dollars from in your reports to the government, you have to have a section on assumptions and technical limitations. Because the problem is the way the peer review culture goes is that if I have a technical limitation section in my paper, the reviewer will just copy and paste it and say reject, right?

But the federal government isn't going to do that, right? NSF isn't going to do that. NSF has already given you the money and you're doing the annual report. And so it has to be, come on, just be honest, right? Like I did not test this method on biological networks and they're very different than social networks. So like caution,

But the federal government isn't going to do that, right? NSF isn't going to do that. NSF has already given you the money and you're doing the annual report. And so it has to be, come on, just be honest, right? Like I did not test this method on biological networks and they're very different than social networks. So like caution,

Yeah, I love that problem. I've thought about that problem a lot. So the issue there is similarity is an eye of the beholder, right? And it depends on the task itself. So similarity is an ill-defined problem. And so you can say, okay, well, I can go with something like an edit distance. Like, okay, how many new nodes do I have to add to graph number two?

Yeah, I love that problem. I've thought about that problem a lot. So the issue there is similarity is an eye of the beholder, right? And it depends on the task itself. So similarity is an ill-defined problem. And so you can say, okay, well, I can go with something like an edit distance. Like, okay, how many new nodes do I have to add to graph number two?

And how many new edges do I have to add or remove to make it look like the other graph? And then try to solve the computationally hard problem of isomorphism. In fact, alignment, right? And in many cases, you don't need alignment, right? So, for example, you can think about two networks and you have started a process of information diffusion on it, like you started a rumor, let's say, right?

And how many new edges do I have to add or remove to make it look like the other graph? And then try to solve the computationally hard problem of isomorphism. In fact, alignment, right? And in many cases, you don't need alignment, right? So, for example, you can think about two networks and you have started a process of information diffusion on it, like you started a rumor, let's say, right?

And you would just measure, like, how similar does this rumor, the same rumor, travel through network one versus network two? And if like, you know, it travels similarly, let's say, you know, I'm going to throw some jargon, like the stationary distribution of a random walker that is spreading this rumor becomes the same at the end. You would say the networks are similar enough. Right.

And you would just measure, like, how similar does this rumor, the same rumor, travel through network one versus network two? And if like, you know, it travels similarly, let's say, you know, I'm going to throw some jargon, like the stationary distribution of a random walker that is spreading this rumor becomes the same at the end. You would say the networks are similar enough. Right.

And so you don't need to have like the sizes exactly be the same. So it could be, for example, you have a social network of France and a social network of Luxembourg and you start a rumor in France and in Luxembourg. And they are processing the same way. And you would say the networks are similar, even though one is much, much bigger than the other.

And so you don't need to have like the sizes exactly be the same. So it could be, for example, you have a social network of France and a social network of Luxembourg and you start a rumor in France and in Luxembourg. And they are processing the same way. And you would say the networks are similar, even though one is much, much bigger than the other.

Yeah, yeah, now the problem with grouping nodes, this is a very important problem and it's been studied by lots of people. Within graphs, it's called community detection. Basically you want to group similar nodes together. Now you can have different functions that you define about what similarity there means. It could mean that these people just talk to each other more, right?

Yeah, yeah, now the problem with grouping nodes, this is a very important problem and it's been studied by lots of people. Within graphs, it's called community detection. Basically you want to group similar nodes together. Now you can have different functions that you define about what similarity there means. It could mean that these people just talk to each other more, right?

So there's more connections between them than what you would expect in a random world, right? or just more connections between them than other folks. Now, this kind of community detection, Aaron Closet, who's a professor at Colorado, showed that there's no free lunch theorem there. And actually, it was Aaron Closet and others. And I think actually Aaron was the last author.

So there's more connections between them than what you would expect in a random world, right? or just more connections between them than other folks. Now, this kind of community detection, Aaron Closet, who's a professor at Colorado, showed that there's no free lunch theorem there. And actually, it was Aaron Closet and others. And I think actually Aaron was the last author.

So I think the first author is Leto Peel. But you know how it is. You usually just name your friend.

So I think the first author is Leto Peel. But you know how it is. You usually just name your friend.

My apologies to the other authors. But they showed it in no free lunch theorem, which basically means that it is not the case that there is like one particular group of or one particular collection of nodes that you're grouping that would give you the best or the best. true communities. You see what I mean?

My apologies to the other authors. But they showed it in no free lunch theorem, which basically means that it is not the case that there is like one particular group of or one particular collection of nodes that you're grouping that would give you the best or the best. true communities. You see what I mean?