PODS 2020: Keynote Talks
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data
Speaker: Martin Grohe (RWTH Aachen University, Germany)
Abstract
Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques
to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have
received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have beenused in practice, in this talk we propose two theoretical approaches that we see as central for understanding the foundations
of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
Bio
Martin Grohe is a computer scientist known for his research on parameterized complexity, mathematical logic, finite model theory, the logic of graphs, database theory, and descriptive complexity theory. He is a University Professor of Computer Science at RWTH Aachen University, where he holds the Chair for Logic and Theory of Discrete Systems. Grohe won the Heinz Maier-Leibnitz Prize awarded by the German Research Foundation in 1999. He was elected as an ACM Fellow in 2017 for "contributions to logic in computer science, database theory, algorithms, and computational complexity.
Slides of the keynote talk can be found
here.
