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SUMMARY:Curvature-based analysis of complex networks
DTSTART:20180328T161500
DTEND:20180328T171500
DTSTAMP:20260510T105754Z
UID:d1f41038f50eedf9751d32c8af105250d5d7f88efe5d7f45d831c1fe
CATEGORIES:Conferences - Seminars
DESCRIPTION:Melanie Weber (Princeton)\nComplex networks are popular means 
 for studying a wide variety of systems across the social and natural scien
 ces. Recent technological advances allow for a description of these system
 s on an unprecedented scale. However\, due to the immense size and complex
 ity of the resulting networks\, efficient evaluation remains a data-analyt
 ic challenge. In a recent series of articles\, we developed geometric tool
 s for efficiently analyzing the structure and evolution of complex network
 s. The core component of our theory\, a discrete Ricci curvature\, transla
 tes central tools from differential geometry to the discrete realm. With t
 hese tools\, we extend the commonly used node-based approach to include ed
 ge-based information such as edge weights and directionality for a more co
 mprehensive network characterization.The analysis of a wide range of compl
 ex networks suggests connections between curvature and higher order networ
 k structure. Our results identify important structural features\, includin
 g long-range connections of high curvature acting as bridges between major
  network components. Thus\, curvature identifies the network’s core stru
 cture on which expensive network hypothesis testing and further network an
 alysis becomes more feasible. We will discuss an application of curvature-
 based methods to networks constructed from fMRI data. Joint work with E. S
 aucan (Technion) and J. Jost (MPI MIS).
LOCATION:CM 0 9 https://plan.epfl.ch/?room=CM09
STATUS:CONFIRMED
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