-- Copyright 2011 by Jannis Pohlmann -- Copyright 2012 by Till Tantau -- -- This file may be distributed and/or modified -- -- 1. under the LaTeX Project Public License and/or -- 2. under the GNU Public License -- -- See the file doc/generic/pgf/licenses/LICENSE for more information -- @release $Header$ local SpringHu2006 = {} -- Imports local declare = require("pgf.gd.interface.InterfaceToAlgorithms").declare --- declare { key = "spring Hu 2006 layout", algorithm = SpringHu2006, preconditions = { connected = true, loop_free = true, simple = true, }, old_graph_model = true, summary = [[" Implementation of a spring graph drawing algorithm based on a paper by Hu. "]], documentation = [[" \begin{itemize} \item Y. Hu. \newblock Efficient, high-quality force-directed graph drawing. \newblock \emph{The Mathematica Journal}, 2006. \end{itemize} There are some modifications compared to the original algorithm, see the Diploma thesis of Pohlmann for details. "]] } -- Imports local PathLengths = require "pgf.gd.lib.PathLengths" local Vector = require "pgf.gd.deprecated.Vector" local CoarseGraph = require "pgf.gd.force.CoarseGraph" local lib = require("pgf.gd.lib") function SpringHu2006:run() -- Setup some parameters local options = self.digraph.options self.iterations = options['iterations'] self.cooling_factor = options['cooling factor'] self.initial_step_length = options['initial step length'] self.convergence_tolerance = options['convergence tolerance'] self.natural_spring_length = options['node distance'] self.coarsen = options['coarsen'] self.downsize_ratio = options['downsize ratio'] self.minimum_graph_size = options['minimum coarsening size'] -- Setup self.downsize_ratio = math.max(0, math.min(1, tonumber(self.downsize_ratio))) self.graph_size = #self.graph.nodes self.graph_density = (2 * #self.graph.edges) / (#self.graph.nodes * (#self.graph.nodes - 1)) -- validate input parameters assert(self.iterations >= 0, 'iterations (value: ' .. self.iterations .. ') need to be greater than 0') assert(self.cooling_factor >= 0 and self.cooling_factor <= 1, 'the cooling factor (value: ' .. self.cooling_factor .. ') needs to be between 0 and 1') assert(self.initial_step_length >= 0, 'the initial step length (value: ' .. self.initial_step_length .. ') needs to be greater than or equal to 0') assert(self.convergence_tolerance >= 0, 'the convergence tolerance (value: ' .. self.convergence_tolerance .. ') needs to be greater than or equal to 0') assert(self.natural_spring_length >= 0, 'the natural spring dimension (value: ' .. self.natural_spring_length .. ') needs to be greater than or equal to 0') assert(self.downsize_ratio >= 0 and self.downsize_ratio <= 1, 'the downsize ratio (value: ' .. self.downsize_ratio .. ') needs to be between 0 and 1') assert(self.minimum_graph_size >= 2, 'the minimum coarsening size of coarse graphs (value: ' .. self.minimum_graph_size .. ') needs to be greater than or equal to 2') -- initialize node weights for _,node in ipairs(self.graph.nodes) do node.weight = 1 end -- initialize edge weights for _,edge in ipairs(self.graph.edges) do edge.weight = 1 end -- initialize the coarse graph data structure. note that the algorithm -- is the same regardless whether coarsening is used, except that the -- number of coarsening steps without coarsening is 0 local coarse_graph = CoarseGraph.new(self.graph) -- check if the multilevel approach should be used if self.coarsen then -- coarsen the graph repeatedly until only minimum_graph_size nodes -- are left or until the size of the coarse graph was not reduced by -- at least the downsize ratio configured by the user while coarse_graph:getSize() > self.minimum_graph_size and coarse_graph:getRatio() <= (1 - self.downsize_ratio) do coarse_graph:coarsen() end end if self.coarsen then -- use the natural spring length as the initial natural spring length local spring_length = self.natural_spring_length -- compute a random initial layout for the coarsest graph self:computeInitialLayout(coarse_graph.graph, spring_length) -- set the spring length to the average edge length of the initial layout spring_length = 0 for _,edge in ipairs(coarse_graph.graph.edges) do spring_length = spring_length + edge.nodes[1].pos:minus(edge.nodes[2].pos):norm() end spring_length = spring_length / #coarse_graph.graph.edges -- additionally improve the layout with the force-based algorithm -- if there are more than two nodes in the coarsest graph if coarse_graph:getSize() > 2 then self:computeForceLayout(coarse_graph.graph, spring_length, SpringHu2006.adaptive_step_update) end -- undo coarsening step by step, applying the force-based sub-algorithm -- to every intermediate coarse graph as well as the original graph while coarse_graph:getLevel() > 0 do -- compute the diameter of the parent coarse graph local parent_diameter = PathLengths.pseudoDiameter(coarse_graph.graph) -- interpolate the previous coarse graph from its parent coarse_graph:interpolate() -- compute the diameter of the current coarse graph local current_diameter = PathLengths.pseudoDiameter(coarse_graph.graph) -- scale node positions by the quotient of the pseudo diameters for _,node in ipairs(coarse_graph.graph) do node.pos:update(function (n, value) return value * (current_diameter / parent_diameter) end) end -- compute forces in the graph self:computeForceLayout(coarse_graph.graph, spring_length, SpringHu2006.conservative_step_update) end else -- compute a random initial layout for the coarsest graph self:computeInitialLayout(coarse_graph.graph, self.natural_spring_length) -- set the spring length to the average edge length of the initial layout spring_length = 0 for _,edge in ipairs(coarse_graph.graph.edges) do spring_length = spring_length + edge.nodes[1].pos:minus(edge.nodes[2].pos):norm() end spring_length = spring_length / #coarse_graph.graph.edges -- improve the layout with the force-based algorithm self:computeForceLayout(coarse_graph.graph, spring_length, SpringHu2006.adaptive_step_update) end local avg_spring_length = 0 for _,edge in ipairs(self.graph.edges) do avg_spring_length = avg_spring_length + edge.nodes[1].pos:minus(edge.nodes[2].pos):norm() end avg_spring_length = avg_spring_length / #self.graph.edges end function SpringHu2006:computeInitialLayout(graph, spring_length) -- TODO how can supernodes and fixed nodes go hand in hand? -- maybe fix the supernode if at least one of its subnodes is -- fixated? -- fixate all nodes that have a 'desired at' option. this will set the -- node.fixed member to true and also set node.pos.x and node.pos.y self:fixateNodes(graph) if #graph.nodes == 2 then if not (graph.nodes[1].fixed and graph.nodes[2].fixed) then local fixed_index = graph.nodes[2].fixed and 2 or 1 local loose_index = graph.nodes[2].fixed and 1 or 2 if not graph.nodes[1].fixed and not graph.nodes[2].fixed then -- both nodes can be moved, so we assume node 1 is fixed at (0,0) graph.nodes[1].pos.x = 0 graph.nodes[1].pos.y = 0 end -- position the loose node relative to the fixed node, with -- the displacement (random direction) matching the spring length local direction = Vector.new{x = lib.random(1, spring_length), y = lib.random(1, spring_length)} local distance = 1.8 * spring_length * self.graph_density * math.sqrt(self.graph_size) / 2 local displacement = direction:normalized():timesScalar(distance) graph.nodes[loose_index].pos = graph.nodes[fixed_index].pos:plus(displacement) else -- both nodes are fixed, initial layout may be far from optimal end else -- use a random positioning technique local function positioning_func(n) local radius = 2 * spring_length * self.graph_density * math.sqrt(self.graph_size) / 2 return lib.random(-radius, radius) end -- compute initial layout based on the random positioning technique for _,node in ipairs(graph.nodes) do if not node.fixed then node.pos.x = positioning_func(1) node.pos.y = positioning_func(2) end end end end function SpringHu2006:computeForceLayout(graph, spring_length, step_update_func) -- global (=repulsive) force function function repulsive_force(distance, graph_distance, weight) --return (1/4) * (1/math.pow(graph_distance, 2)) * (distance - (spring_length * graph_distance)) return (distance - (spring_length * graph_distance)) end -- fixate all nodes that have a 'desired at' option. this will set the -- node.fixed member to true and also set node.pos.x and node.pos.y self:fixateNodes(graph) -- adjust the initial step length automatically if desired by the user local step_length = self.initial_step_length == 0 and spring_length or self.initial_step_length -- convergence criteria etc. local converged = false local energy = math.huge local iteration = 0 local progress = 0 -- compute graph distance between all pairs of nodes local distances = PathLengths.floydWarshall(graph) while not converged and iteration < self.iterations do -- remember old node positions local old_positions = lib.map(graph.nodes, function (node) return node.pos:copy(), node end) -- remember the old system energy and reset it for the current iteration local old_energy = energy energy = 0 for _,v in ipairs(graph.nodes) do if not v.fixed then -- vector for the displacement of v local d = Vector.new(2) for _,u in ipairs(graph.nodes) do if v ~= u then -- compute the distance between u and v local delta = u.pos:minus(v.pos) -- enforce a small virtual distance if the nodes are -- located at (almost) the same position if delta:norm() < 0.1 then delta:update(function (n, value) return 0.1 + lib.random() * 0.1 end) end local graph_distance = (distances[u] and distances[u][v]) and distances[u][v] or #graph.nodes + 1 -- compute the repulsive force vector local force = repulsive_force(delta:norm(), graph_distance, v.weight) local force = delta:normalized():timesScalar(force) -- move the node v accordingly d = d:plus(force) end end -- really move the node now -- TODO note how all nodes are moved by the same amount (step_length) -- while Walshaw multiplies the normalized force with min(step_length, -- d:norm()). could that improve this algorithm even further? v.pos = v.pos:plus(d:normalized():timesScalar(step_length)) -- update the energy function energy = energy + math.pow(d:norm(), 2) -- vector for the displacement of v local d = Vector.new(2) for _,u in ipairs(graph.nodes) do if v ~= u then -- compute the distance between u and v local delta = u.pos:minus(v.pos) -- enforce a small virtual distance if the nodes are -- located at (almost) the same position if delta:norm() < 0.1 then delta:update(function (n, value) return 0.1 + lib.random() * 0.1 end) end local graph_distance = (distances[u] and distances[u][v]) and distances[u][v] or #graph.nodes + 1 -- compute the repulsive force vector local force = repulsive_force(delta:norm(), graph_distance, v.weight) local force = delta:normalized():timesScalar(force) -- move the node v accordingly d = d:plus(force) end end -- really move the node now -- TODO note how all nodes are moved by the same amount (step_length) -- while Walshaw multiplies the normalized force with min(step_length, -- d:norm()). could that improve this algorithm even further? v.pos = v.pos:plus(d:normalized():timesScalar(step_length)) -- update the energy function energy = energy + math.pow(d:norm(), 2) end end -- update the step length and progress counter step_length, progress = step_update_func(step_length, self.cooling_factor, energy, old_energy, progress) -- compute the maximum node movement in this iteration local max_movement = 0 for _,x in ipairs(graph.nodes) do local delta = x.pos:minus(old_positions[x]) max_movement = math.max(delta:norm(), max_movement) end -- the algorithm will converge if the maximum movement is below a -- threshold depending on the spring length and the convergence -- tolerance if max_movement < spring_length * self.convergence_tolerance then converged = true end -- increment the iteration counter iteration = iteration + 1 end end -- Fixes nodes at their specified positions. -- function SpringHu2006:fixateNodes(graph) local number_of_fixed_nodes = 0 for _,node in ipairs(graph.nodes) do -- read the 'desired at' option of the node local coordinate = node:getOption('desired at') if coordinate then -- apply the coordinate node.pos.x = coordinate.x node.pos.y = coordinate.y -- mark the node as fixed node.fixed = true number_of_fixed_nodes = number_of_fixed_nodes + 1 end end if number_of_fixed_nodes > 1 then self.growth_direction = "fixed" -- do not grow, orientation is now fixed end end function SpringHu2006.conservative_step_update(step, cooling_factor) return cooling_factor * step, nil end function SpringHu2006.adaptive_step_update(step, cooling_factor, energy, old_energy, progress) if energy < old_energy then progress = progress + 1 if progress >= 5 then progress = 0 step = step / cooling_factor end else progress = 0 step = cooling_factor * step end return step, progress end -- done return SpringHu2006