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Creators/Authors contains: "Chiu, Alvin"

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  1. ; ; (, Springer)
    Accepted Manuscript Full Text Available
  2. ; ; (, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)
    Felsner, Stefan; Klein, Karsten (Ed.)
    We investigate force-directed graph drawing techniques under the constraint that some nodes must be anchored to stay within a given polygonal region associated with it (i.e. some positional information is known). The low energy layouts produced by such algorithms may reveal geographic information about nodes with no such knowledge a priori. Some applications of graph drawing with partial positional information include location-based social networks and rail networks, where the geographical locations need not be precise. 
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    Full Text Available 
  3. ; ; ; ; ; (, ArXiv)
    Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a sparsifier. The proposed method consistently outperforms several standard approximation algorithms on different types of graphs and often finds the optimal solution. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; (, Graphs and Combinatorics)
    null (Ed.)
    Full Text Available