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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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TZID:Europe/Paris
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DTSTART:20191027T030000
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DTSTART:20200329T020000
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UID:calendar.18962.field_data.0@www.glad.uniroma1.it
DTSTAMP:20260404T173111Z
CREATED:20191202T090220Z
DESCRIPTION:Graph clustering is a fundamental problem in graph mining with 
 important applications ranging from social networks analysis and entity re
 solution to computer vision and semi-supervised learning. In this talk I w
 ill discuss two novel variations of graph clustering. In the first part of
  the talk I will discuss the following clustering problem: there exist two
  latent clusters\, and we are allowed to query any pair of nodes whether t
 hey belong to the same cluster or not\, but the answer to the query is cor
 rupted with some probability less than 1/2. Can we recover the clusters wi
 th high probability and what is the minimum number of queries needed? I wi
 ll present two state-of-the-art algorithms and a closely related applicati
 on on predicting signs in online social networks. Our results improve rece
 nt results by Mazumdar and Saha [NIPS’17]. In the second part of the talk\
 , I will discuss how to exploit motifs to uncover communities. Specificall
 y\, I will introduce the concept of motif-expanders that serves as the bas
 is for motif-based clustering. I will present efficient algorithms and a w
 ell-performing heuristic that outperforms some of the most popular graph c
 lustering tools. I will conclude with some experimental results that show 
 the effectiveness of our methods to multiple applications in machine learn
 ing and graph mining. BioBabis Tsourakakis is an assistant professor in co
 mputer science at Boston University and a research associate at Harvard. T
 sourakakis obtained his PhD in Algorithms\, Combinatorics and Optimization
  at Carnegie Mellon under the supervision of Alan Frieze\, was a postdocto
 ral fellow at Brown University and Harvard under the supervision of Eli Up
 fal and Michael Mitzenmacher respectively. Before joining Boston Universit
 y\, he worked as a researcher in the Google Brain team. He won a best pape
 r award in IEEE Data Mining\, has delivered three tutorials in the ACM SIG
 KDD Conference on Knowledge Discovery and Data Mining\, and has designed t
 wo graph mining libraries for large-scale graph mining\, one of which has 
 been officially included in Windows Azure. His research focuses on large-s
 cale graph mining\, and machine learning.   
DTSTART;TZID=Europe/Paris:20191209T120000
DTEND;TZID=Europe/Paris:20191209T120000
LAST-MODIFIED:20191202T113248Z
LOCATION:Aula Magna\, DIAG\, VIA Ariosto 25
SUMMARY:Graph Clustering with Noisy Queries and Motifs  - Charalampos Tsour
 akakis (Boston University and Harvard)
URL;TYPE=URI:http://www.glad.uniroma1.it/node/18962
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