Desinforrmatie over vaccins, klimaat of corona: weinig tegen te doen, was lang de gedachte. Een groeiende groep wetenschappers denkt toch een methode op het spoor te zijn. De sleutel: zie de... Show moreDesinforrmatie over vaccins, klimaat of corona: weinig tegen te doen, was lang de gedachte. Een groeiende groep wetenschappers denkt toch een methode op het spoor te zijn. De sleutel: zie de misleidende informatie zélf als een virus. Show less
This thesis is about algorithms for analyzing large real-world graphs (or networks). Examples include (online) social networks, webgraphs, information networks, biological networks and scientific... Show moreThis thesis is about algorithms for analyzing large real-world graphs (or networks). Examples include (online) social networks, webgraphs, information networks, biological networks and scientific collaboration and citation networks. Although these graphs differ in terms of what kind of information the objects and relationships represent, it turns out that the structure of each these networks is surprisingly similar.For computer scientists, there is an obvious challenge to design efficient algorithms that allow large graphs to be processed and analyzed in a practical setting, facing the challenges of processing millions of nodes and billions of edges. Specifically, there is an opportunity to exploit the non-random structure of real-world graphs to efficiently compute or approximate various properties and measures that would be too hard to compute using traditional graph algorithms. Examples include computation of node-to-node distances and extreme distance measures such as the exact diameter and radius of a graph. Show less
