Knowledge Discovery from Graphs and Networks

In this talk, I will present some recent research directions that I have been exploring. One common denominator is the notion of graph or network. I'll start by describing my activities in the field of knowledge graphs (KG), graphs organized as a set of triples of the form (subject, predicate, object), where the predicate denotes some semantic relationship between the subject and the object (e.g., Stanley Kubrick, director, A Clockwork Orange). I'll discuss why existing approaches to learning low-level representations (or embeddings) for subject/object and predicates are sub-optimal when it comes to learning representations of triples as a whole; I'll show how to transform a KG into its triple-centric version by taking into account the semantics edges. Hence, I will describe two triple embedding learning architectures useful for downstream tasks such as triple verification; one based on biased random walks and the other based on graph neural networks. Next, I will discuss how to improve any existing semantic-oblivion embedding approach based on random walks by superimposing an abstract notion of neighborhood, based on an arbitrary node similarity measure. Finally, in the landscape of networks, I will describe ongoing research activities on a new topic called community deception, which is about how to hide a community (set of nodes) from social network analysis tools. I'll discuss some techniques based on carefully selected edge updates and their extension to attributed networks.


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