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>> Dengyong Zhou: Pleased to have Jungming as our speaker today. Jung Ming is a lane fellow
of the computer science school over at Carnegie Mellon University. Prior to this, he is a Ph.D.
student at UC Berkeley computer science department. His advisor was Michael Jordan and Yun
Song. Jungming will stay here until Friday and if you want to meet him, please drop me a note.
Jungming will join the university of Arizona as faculty.
>> Jungming Yin: So I would like to begin by thanking Denny for having me here and nice
introduction. So I just got on this topic of network analysis quite recently, and I'm going to share
some ideas on how to discover and explore overlapping community structures in large networks.
And this is joint work with my collaborators, Qirong Ho and Eric Xing from CMU.
So as everyone knows, so we are now living in a network [indiscernible] and networks basically
are mathematic abstractions and they're popular representations of [indiscernible] when nodes are
individuals and edges are relationships between individuals. So they have been studied in social
science, information science, biology, economics and so on.
So there are many interesting problems and questions in network analysis. So you probably heard
about the [indiscernible] distribution of the node degrees and the small word phenomenon in
ranking pages and so on, so forth.
So I'm going to talk about another important problem in network analysis, which is how to
effectively to detect community structure in the large network with the hope to identify, like, sets
of the individuals with common functionalities, roles or properties. And being able to do so will
help us to understand and explore the networks more effectively.
So let's look at some simple examples. So in social networks, the nodes are users and edges are
friendships. And the based on the network topology or connectivities, so can we identify some
social groupings, perhaps by their background or maybe by their interests. So in reality, each
individual might belong to multiple communities, and it's linked where we flag these multiple
memberships. So, for example, an individual might be connected to colleagues, school mates,
family members, and friends. And so unless that someone like who doesn't make friends with
your family members or your colleagues, which I think is rarely the case, and then there's
community structures will very be likely to overlap with each other.
And the second example is protein-protein interaction network from biology, where each edge
represents a physical interaction between two proteins. So in this case, each current community is
a function of modules that consists of a set of proteins with common functionalities. AndSlide 7