Hi list. I am using graph-tool to model an /apical junctions/ network, based on the model by Farhadifar et al. <http://www.sciencedirect.com/science/article/pii/S0960982207023342>. In short, it corresponds to the outer surface of the cells in a particular region of the embryo, where each cell is represented by a polygon. Topology of the network changes due to cell division and cell death. The physical model involves the local minimization of an energy depending on the local geometry. I got a working model, but the energy optimization is time consuming. I think that this is due to the way I compute the components of the vector formed by each edge of the graph, by iterating over the edges., eg: |for edgein graph.edges(): v0, v1 = edge.source(), edge.target() deltax[edge] = x[v1] - x[v0] deltay[edge] = y[v1] - y[v0]| This is further complicated (to a little extent) by periodic conditions to the coordinate systems, and some other details, but I think this is the core issue. I have the feeling I can do better, perhaps by using the adjacency matrix? This would be particularly interesting as the graph topology doesn't change for a given optimization. Any advice is welcome, thanks! Guillaume