Sublinear Algorithms in Social Networks via Core-Periphery Decomposition

Explore a 40-minute lecture on sublinear algorithms in social networks, focusing on core-periphery decomposition. Delve into the research of Omri Ben-Eliezer from the Simons Institute as he discusses practical sublinear algorithms for large networks. Examine how structural characteristics like heavy-tailed degree distribution and core-periphery structure can be leveraged to design fast algorithms. Learn about a hierarchical core-periphery decomposition data structure that requires sublinear preprocessing and accelerates tasks such as node sampling, triangle sampling, and shortest path computations. Gain insights into the balance between theoretical justifications and empirical results in this domain. Discover the collaborative efforts behind this research, involving work with Sabyasachi Basu, Talya Eden, Dimitris Fotakis, Nadia Koshima, Joel Oren, Tim Rieder, and C. Seshadhri.