Tina Eliassi-Rad
speaker
154 appearances
1 recordings
1 series
first heard Jan 2025
last heard Jan 2025
Tina Eliassi-Rad’s voice in public audio — every appearance, attributed to the second.
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Appearances
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?
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.
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?
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?
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.
And like when I would do searches there, and this is many years ago, I would get more like physics books than like when I lived elsewhere. They would sell me they wouldn't show me as much physics books, right, just based on the location, the zip code. And so there's some of that that's going on. And I feel like that is more of the problem of like not really serving the individual or exploring.
as much as possible.
Yeah, so, you know, it depends on what kind of network it is, right? So in social networks, for example, we know that there are two dominant processes that form social networks. One is closing of what we're calling wedges. So if I am friends with you and you are friends with Jennifer, then I will become friends with Jennifer, right? We close that triangle.
And in fact, if you and I have, for example, many common friends, or let's say me and Jennifer in my example, we have many common friends and we are not friends, then there is something going on, that there was lots of opportunities that we could become friends, but we chose not to become friends, right?
Now, there's also, of course, partial observability in that, like, maybe I didn't observe it, right? However big your data is, you're not omniscient, you don't see things, right? But we do expect that friend of a friend is also a friend. That's one. The other one is this notion of preferential attachment, right? That everybody wants to connect to a star.
And so you're interested in like, basically those are the two big patterns. And then you look at deviations from that. So a work that was done by John Kleinberg at Cornell is, He's a very well-known computer science professor. This is a while back, was think Facebook, for example. Who is your romantic partner on Facebook?
And he and his colleagues showed that basically you are the center of a flower and you have petals around you. These petals could be your high school buddies or college buddies, etc. They have just more triangles in them. And people who fall outside of these petals and have a lot of connections to these petals are either your sibling or your romantic partner.
That is, you are introducing them to other facets of your life. And they show that when that connections stopped, establishment of those connections stopped, it's a leading indicator that you will break up.
Yeah. So you were talking about which connections to pay attention to, right? It's like, so those are some of the things that are fun when you look at social networks. I mean, biological networks are totally different. So in biological networks, it's a whole other ball of wax. There's not like, you're not looking for common friends.
You're looking more for like complementarity between different proteins that serve some function.
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