Tina Eliassi-Rad

Thank you. Thank you for having me.

Thank you. Thank you for having me.

Well, when you're trying to understand the phenomena, usually you have multiple entities, like multiple people, and they have relationships with each other, right?

Well, when you're trying to understand the phenomena, usually you have multiple entities, like multiple people, and they have relationships with each other, right?

And so when we're looking at graph, like machine learning with graphs or graph mining, we're trying to find those, what we're calling relational dependencies, that like the probability of you and me being friends, given that we both like Apple products, is greater than the probability of you and me just being friends.

And so when we're looking at graph, like machine learning with graphs or graph mining, we're trying to find those, what we're calling relational dependencies, that like the probability of you and me being friends, given that we both like Apple products, is greater than the probability of you and me just being friends.

Or the probability of me liking Apple products, given that we're friends, is more than the probability the prior probability of each of us liking an Apple product. So the second one that is, we are friends, you influence me. And so I like Apple products and I buy Apple products or I buy this headphone, right? Headset. And the first one is that because we like similar things, we become friends.

Or the probability of me liking Apple products, given that we're friends, is more than the probability the prior probability of each of us liking an Apple product. So the second one that is, we are friends, you influence me. And so I like Apple products and I buy Apple products or I buy this headphone, right? Headset. And the first one is that because we like similar things, we become friends.

This notion of homophily or like birds of a feather flock together. But in a nutshell, like people who work on, Machine learning on graphs, network scientists who are interested in understanding phenomena, network sciences and interdisciplinary discipline. It is about these relational dependencies and like, what can we find? What are the patterns?

This notion of homophily or like birds of a feather flock together. But in a nutshell, like people who work on, Machine learning on graphs, network scientists who are interested in understanding phenomena, network sciences and interdisciplinary discipline. It is about these relational dependencies and like, what can we find? What are the patterns?

What are the anomalies in the relationships that get formed?

What are the anomalies in the relationships that get formed?

Yeah, there's some of that. I would say that, so I have this thing I call the paradox of big data, which is like there's a lot of data, but to predict specifically for what Tina wants, it's difficult, right? You don't have maybe as much information about Tina.

Yeah, there's some of that. I would say that, so I have this thing I call the paradox of big data, which is like there's a lot of data, but to predict specifically for what Tina wants, it's difficult, right? You don't have maybe as much information about Tina.

Now, if Tina belongs into some majority group, then maybe you can aggregate from the majority and say, well, Tina is part of this flock, and so Tina will like whatever this flock likes, right? Um, but really I feel like the problem these days is more about, uh, exploitation and going with things that are popular, um, than, um, exploration, right?

Now, if Tina belongs into some majority group, then maybe you can aggregate from the majority and say, well, Tina is part of this flock, and so Tina will like whatever this flock likes, right? Um, but really I feel like the problem these days is more about, uh, exploitation and going with things that are popular, um, than, um, exploration, right?

Like in the past we would go to the library or the bookstore and you're looking for a book and you would find other things. And those were, you know, they basically did that. The cherry on top of the cake. Right. The cream is like, oh, yeah, I found this. Right. And now we're really not getting that. Right.

Like in the past we would go to the library or the bookstore and you're looking for a book and you would find other things. And those were, you know, they basically did that. The cherry on top of the cake. Right. The cream is like, oh, yeah, I found this. Right. And now we're really not getting that. Right.

So when you use all these recommendation systems, whether it's Google or any other Amazon, et cetera, they oftentimes show you what is popular or what they believe you would like. Right. So in a past life, I worked at Lawrence Livermore National Laboratory, which is a physics laboratory.

So when you use all these recommendation systems, whether it's Google or any other Amazon, et cetera, they oftentimes show you what is popular or what they believe you would like. Right. So in a past life, I worked at Lawrence Livermore National Laboratory, which is a physics laboratory.