=== Quando ===
Mercoledì, primo giugno ore 14:00

=== Dove ===
  Dipartimento di Ingegneria
  Sezione di Informatica e Automazione
  Via della Vasca Navale, 79
  Sala riunioni  – Primo piano

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SEMINARIO #1

=== Title ===
  NetFork: Mapping Time to Space in Network Visualization

=== Speaker ===
  Valentino Di Donato
  PhD Student in Computer Science and Automation
  Roma Tre University
  http://www.dia.uniroma3.it/~compunet/www/view/person.php?id=didonato

=== Abstract ===
Dynamic network visualization aims at representing the evolution of relational information in a readable, scalable, and effective way. A natural approach, called ‘time-to-time mapping’, consists of computing a representation of the network at each time step and animating the transition between subsequent time steps. However, recent literature recommends to represent time-related events by means of static graphic counterparts, realizing the so called ‘time-to-space mapping’. This paradigm has been successfully applied to networks where nodes and edges are subject to a restricted set of events: appearances, disappearances, and attribute changes. In this paper we describe NETFORK, a system that conveys the timings and the impact of path changes that occur in a routing network by suitable time-to-space metaphors, without relying on the time-to-time mapping adopted by the play-back interfaces of alternative network monitoring tools. A user study and a comparison with the state of the art show that users can leverage on high level static representations to quickly assess the quantity and quality of the path dynamics that took place in the network.

Joint work with M. Patrignani and C. Squarcella

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SEMINARIO #2

=== Title ===
  There is More to Streamgraphs than Movies: Better Aesthetics via Ordering and Lassoing

=== Speaker ===
  Marco Di Bartolomeo
  PhD Student in Computer Science and Automation
  Roma Tre University
  http://www.dia.uniroma3.it/~compunet/www/view/person.php?id=mdb

=== Abstract ===
Streamgraphs were popularized in 2008 when The New York Times used them to visualize box office revenues for 7500 movies over 21 years. The aesthetics of a streamgraph is affected by three components: the ordering of the layers, the shape of the
lowest curve of the drawing, known as the baseline, and the labels for the layers. As of today, the ordering and baseline computation algorithms proposed in the paper of Byron and Wattenberg are still considered the state of the art. However, their
ordering algorithm exploits statistical properties of the movie revenue data that may not hold in other data. In addition, the baseline optimization is based on a definition of visual energy that in some cases results in considerable amount of visual
distortion. We offer an ordering algorithm that works well regardless of the properties of the input data, and propose a 1-norm based definition of visual energy and the associated solution method that overcomes the limitation of the original baseline
optimization procedure. Furthermore, we propose an efficient layer labeling algorithm that scales linearly to the data size in place of the brute-force algorithm adopted by Byron and Wattenberg. We demonstrate the advantage of our algorithms over
existing techniques on a number of real world data sets.

Joint work with Yifan Hu