experiments.

Both our SA and simple local optimization algorithm use an adapted error function for an edge that includes the weighting function above. In our simple local optimizer, we additionally change the sorting of the edges such that edges with higher confidence are considered first. Confidence ties are broken by choosing the edge with larger error first.

Implementation Details {#sec:impl}
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##### Initial layout. {#initial-layout. .unnumbered}

For computing the initial layout, we implemented three algorithms. In addition to PivotMDS [@brandes], which was used by Gansner [*et al.*]{} [@gansner2013], these are two random vertex placement algorithms. The first one is a very simple method and places the coordinates randomly in a $k$-dimensional hypercube with predefined side length. The second one does include some of the distance information provided by the input. Given an edge $\{v,w\}$ and the coordinates of vertex $v$, we place $w$ at the boundary of a $k$-dimensional hypersphere with radius $d_{vw}$ and centered at $v$. The algorithm needs a start vertex with given coordinates, as neighbors will be placed around it. We choose the vertex with maximum degree in the graph; this way, the highest number of neighbors is placed in random directions around the start vertex, which 