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UID:554@lincs.fr
DTSTART;TZID=Europe/Paris:20200922T090000
DTEND;TZID=Europe/Paris:20200922T130000
DTSTAMP:20200910T120214Z
URL:https://www.lincs.fr/events/thesis-defense-algorithmic-and-software-co
 ntributions-to-graphmining/
SUMMARY:Thesis defense: "Algorithmic and Software Contributions to Graph
 Mining"
DESCRIPTION:Since the introduction of Google’s Page Rank method for Web
 searches in the late 1990s\, graph algorithms have been part of our daily
 lives. In the mid 2000s\, the arrival of social networks has amplified this
 phenomenon\, creating new use-cases for these algorithms. Relationships
 between entities can be of multiple types: user-user symmetric
 relationships for Facebook or LinkedIn\, follower-followee asymmetric ones
 for Twitter or even user-content bipartite ones for Netflix\, Deezer or
 Amazon. They all come with their own challenges and the applications are
 numerous: centrality calculus for influence measurement\, node clustering
 for knowledge discovery\, node classification for recommendation or
 embedding for link prediction\, to name a few. In the meantime\, the
 context in which graph algorithms are applied has rapidly become more
 constrained. On the one hand\, the increasing size of the datasets with
 millions of entities\, and sometimes billions of relationships\, bounds the
 asymptotic complexity of the algorithms for industrial applications. On the
 other hand\, as these algorithms affect our daily lives\, there is a
 growing demand for explanability and fairness in the domain of artificial
 intelligence in general. Graph mining is no exception. For example\, the
 European Union has published a set of ethics guidelines for trustworthy AI.
 This calls for further analysis of the current models and even new ones.
 This thesis provides specific answers via a novel analysis of not only
 standard\, but also extensions\, variants\, and original graph algorithms.
 Scalability is taken into account every step of the way. Following what the
 Scikit-learn project does for standard machine learning\, we deem it
 important to make these algorithms available to as many people as possible
 and participate in graph mining popularization. Therefore\, we have
 developed an open-source software\, Scikit-network\, which implements and
 documents the algorithms in a simple and efficient way. With this tool\, we
 cover several areas of graph mining such as graph embedding\, clustering\,
 and semi-supervised node classification.\n\nParticiper à la réunion
 Zoom\nhttps://telecom-paris.zoom.us/j/99270581285?pwd=VUR5Z3gxN2tqamhJc0xnO
 XZYcWxUQT09\n\nID de réunion : 992 7058 1285\nCode secret : 143438
CATEGORIES:PhD Defense
LOCATION:LINCS Seminars room\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.828400;2.356897
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS Seminars
 room:geo:48.828400,2.356897
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