INFORMAL SEMINAR on Statistical Physics: " Who is keeping you in that community? "
It is a common experience that groups of people with tight inter-relations can be easily formed or dissolved. These groups are not however restricted to social environments; they can be found in systems ranging from protein interaction networks to the Web and can be seen as manifestations of complexity. Within the framework of graph theory such groups are known as clusters or communities and many techniques have been proposed to detect them.
In this talk I will show that a recently introduced inference method on networks is able not only to determine clusters but also to measure the extent to which each element influences the group membership of its neighbors. We demonstrate the generality and relevance of this information-based hierarchy with several real-world examples. This is joint work with Jose Javier Ramasco of ISI Torino.
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