Question

我需要在图像上绘制大型数据集，我使用了graphvis命令行和所有可用的工具（dot，neato，twopi等），但结果不可读并且包含重叠。

我需要的是在边缘上显示带有标签的节点，最小重叠，以便图形可读，也可以打印在A4或A3纸上。

我在neato和相同的重叠结果中使用了overlap = false，splines = true选项。

这是数据集：

graph {
    graph [ bgcolor=ivory2, overlap=false, splines=true, ranksep ="2.75"]
    {node [width=1,height=1,shape=circle,style=filled,color=skyblue] "ECNY" }
    edge [ len=2, sep=5] 
    "DANA" -- "HMRN" -- "ECNY" -- "NORI" -- "MAJZ" -- "RSFH" -- "DANA" [label ="LD1-25-A01",  penwidth =3 , color="#156163"]
    "DANA" -- "HMRN" -- "ECNY" -- "NORI" -- "MAJZ" -- "RSFH" -- "DANA" [label ="LD1-25-A02",  penwidth =3 , color="#30a1f9"]
    "DANA" -- "MAJZ" -- "ECNY" -- "HMRN" -- "DANA" [label ="LD1-25-A03",  penwidth =3 , color="#ec591d"]
    "DANA" -- "MAJZ" -- "ECNY" -- "HMRN" -- "DANA" [label ="LD1-25-A04",  penwidth =3 , color="#263a5f"]
    "DANA" -- "ECNY" -- "DANA" [label ="LD3-25-A02",  penwidth =3 , color="#a3517c"]
    "HMRN" -- "ECNY" -- "MAJZ" -- "DANA" -- "HMRN" [label ="LD1-25-H01",  penwidth =3 , color="#800d83"]
    "HMRN" -- "ECNY" -- "MAJZ" -- "DANA" -- "TWRN" -- "HMRN" [label ="LD1-25-H02",  penwidth =3 , color="#89e15a"]
    "HMRN" -- "ECNY" -- "MAJZ" -- "DANA" -- "TWRN" -- "HMRN" [label ="LD3-25-H03",  penwidth =3 , color="#74ed0e"]
    "HMRN" -- "ECNY" -- "HMRN" [label ="JED-10-H08",  penwidth =3 , color="#e8e786"]
    "HMRN" -- "ECNY" -- "MAJZ" -- "DANA" -- "HMRN" [label ="LD1-25-H04",  penwidth =3 , color="#e1f559"]
    "ECNY" -- "HMRN" -- "ECNY" [label ="JED-10-A02",  penwidth =3 , color="#8f7964"]
    "ECNY" -- "HMRN" -- "ECNY" [label ="JED-10-A03",  penwidth =3 , color="#9058f0"]
    "ECNY" -- "HMRN" -- "SBCB" -- "ECNY" [label ="JED-10-A04",  penwidth =3 , color="#b537b7"]
    "ECNY" -- "HMRN" -- "SBCB" -- "ECNY" [label ="JED-10-A05",  penwidth =3 , color="#fc2c2a"]
    "ECNY" -- "HMRN" -- "ECNY" [label ="JED-10-A06",  penwidth =3 , color="#36309c"]
    "ECNY" -- "HMRN" -- "ECNY" [label ="JED-10-A07",  penwidth =3 , color="#25a571"]
    "ECNY" -- "OBHR" -- "ECNY" [label ="JED-10-A26",  penwidth =3 , color="#1a6077"]
    "ECNY" -- "2820" -- "ECNY" [label ="JED-25-A03",  penwidth =3 , color="#8bce8c"]
    "ECNY" -- "2138" -- "2129" -- "ECNY" [label ="JED-25-A04",  penwidth =3 , color="#9b9afa"]
    "ECNY" -- "2017" -- "2013" -- "ECNY" [label ="JED-25-A05",  penwidth =3 , color="#5ea9aa"]
    "ECNY" -- "2027" -- "2128" -- "ECNY" [label ="JED-25-A22",  penwidth =3 , color="#c0c4d4"]
    "ECNY" -- "2130" -- "2137" -- "ECNY" [label ="JED-25-A27",  penwidth =3 , color="#781ce0"]
    "ECNY" -- "DANA" -- "ECNY" [label ="LD3-25-A01",  penwidth =3 , color="#fd5c5a"]
    "ECNY" -- "HMRN" -- "ZJ01" -- "ECNY" [label ="JED-10-H03",  penwidth =3 , color="#32e13b"]
    "ECNY" -- "HMRN" -- "ECNY" [label ="JED-10-H04",  penwidth =3 , color="#487f94"]
    "ECNY" -- "2341" -- "2235" -- "2233" -- "ECNY" [label ="JED-10-H05",  penwidth =3 , color="#82ae2d"]
    "ECNY" -- "HMRN" -- "SBCB" -- "ECNY" [label ="JED-10-H06",  penwidth =3 , color="#f4651c"]
    "ECNY" -- "HMRN" -- "SBCB" -- "ECNY" [label ="JED-10-H07",  penwidth =3 , color="#23dd41"]
    "ECNY" -- "HMRN" -- "OBHR" -- "ECNY" [label ="JED-10-H37",  penwidth =3 , color="#521f43"]
    "ECNY" -- "PROJECT" -- "ECNY" [label ="JED-10-H49",  penwidth =3 , color="#0a4bf1"]
    "ECNY" -- "2234" -- "2246" -- "2245" -- "2320" -- "ECNY" [label ="JED-25-H01",  penwidth =3 , color="#6127e4"]
    "ECNY" -- "2842" -- "2030" -- "ECNY" [label ="JED-25-H03",  penwidth =3 , color="#ce1f98"]
    "ECNY" -- "2170" -- "2166" -- "ECNY" [label ="JED-25-H06",  penwidth =3 , color="#aeb0ce"]
    "ECNY" -- "2158" -- "2144" -- "ECNY" [label ="JED-25-H11",  penwidth =3 , color="#9ef618"]
    "ECNY" -- "5824" -- "2011" -- "ECNY" [label ="JED-25-H15",  penwidth =3 , color="#b2d524"]
    "ECNY" -- "2010" -- "2830" -- "2198" -- "ECNY" [label ="JED-25-H16",  penwidth =3 , color="#53e7ae"]
    "ECNY" -- "2179" -- "ECNY" [label ="JED-25-H17",  penwidth =3 , color="#149169"]
    "ECNY" -- "2211" -- "ECNY" [label ="JED-25-H19",  penwidth =3 , color="#15a51b"]
    "ECNY" -- "2316" -- "ECNY" [label ="JED-25-H20",  penwidth =3 , color="#e91d18"]
    "ECNY" -- "2203" -- "ECNY" [label ="JED-25-H22",  penwidth =3 , color="#38a23a"]
    "ECNY" -- "SBCB" -- "JFCC" -- "ECNY" [label ="JED-25-H33",  penwidth =3 , color="#a1abf4"]
    "ECNY" -- "HMRN" -- "ECNY" [label ="JED-25-H41",  penwidth =3 , color="#c14ff8"]
    "ECNY" -- "TAIF" -- "SNFN" -- "NORI" -- "ECNY" [label ="LD1-10-H04",  penwidth =3 , color="#75fb4f"]
    "ECNY" -- "MAJZ" -- "DANA" -- "TWRN" -- "HMRN" -- "ECNY" [label ="LD1-25-H03",  penwidth =3 , color="#52d1c8"]
    "ECNY" -- "DANA" -- "ECNY" [label ="LD3-25-H01",  penwidth =3 , color="#498a16"]
    "ECNY" -- "DANA" -- "ECNY" [label ="LD3-25-H02",  penwidth =3 , color="#70f831"]
    "ECNY" -- "2310" -- "5880" -- "5301" -- "2248" -- "ECNY" [label ="JED-10-H17",  penwidth =3 , color="#ebb4e1"]
    "SBCB" -- "ECNY" -- "HMRN" -- "JELS" -- "ROMN" -- "OBHR" -- "SAFA" -- "SBCB" [label ="JED-W40-A01",  penwidth =3 , color="#7ff59c"]
    "SBCB" -- "SAFA" -- "OBHR" -- "HMRN" -- "ECNY" -- "SBCB" [label ="JED-10-H35",  penwidth =3 , color="#e817b9"]
    "2171" -- "ECNY" -- "2171" [label ="JED-10-H19",  penwidth =3 , color="#bf1252"]
    "ABHA" -- "ECNY" -- "DANA" -- "ABHA" [label ="LD3-10-H01",  penwidth =3 , color="#8b60ae"]
    "MAJZ" -- "ECNY" -- "DANA" -- "2510" -- "MAJZ" [label ="LD1-10-H02",  penwidth =3 , color="#1e3c55"]
    "2209" -- "2206" -- "ECNY" -- "2209" [label ="JED-25-H02",  penwidth =3 , color="#1b6092"]
    }

这是使用这些选项时的输出之一（在neato中）：

图[bgcolor = ivory2，overlap = false，splines = true，ranksep =“2.75”]

{node [width = 1，height = 1，shape = circle，style = filled，color = skyblue]   “ECNY”}

edge [len = 2，sep = 5]

enter image description here

当make overlap = scale但是图像仍然无法读取时也是这样！

overlap = scale，sep = \“+ 25,25 \”，splines = true，rankdir = \“TB \”

enter image description here

那么其他什么属性可以增强这个图形？

Answer 1

不可避免的是，随着节点数和边数的增加，我们最终将没有足够的空间来获得紧凑但却清晰的图形。但是，我认为，我们可以采取一些措施来改善问题。在这种情况下，我们可以利用所有边缘序列形成返回“ECNY”的循环这一事实。

首先，我将更接近“ECNY”的边加权（使它们更具弹性）并使它们在默认情况下更长（使得图形的中间更加展开），并且与边缘相反离“ECNY”更远。

其次，我只标记了每个循环一次，标签位于边缘，远离循环中的“ECNY”，因为我可以得到它。

我使用Python脚本adjweight.py以编程方式进行了这些更改，如下所示：

import sys, re, math

for line in sys.stdin:
    line = line.rstrip()
    if re.match(' +"',line):
        loop, attr = line.split(" [")
        loopli = loop.split(" -- ")[1:] 
        # We take all but the first node, to eliminate repeats
        # then we put the list of edges together with "ECNY" first and last.
        i = loopli.index('"ECNY"')
        loopli = loopli[i:] + loopli[:i] + [loopli[i]]
        n = len(loopli)

        # Now we write the multi-edge lines as individual lines so that we can 
        # give each edge an individual weight and length

        for i in range(n-1):
            # We weight edges that are furthest from the center (i.e. those with 
            # numbers closest to n/2) highest and those that are closest to the
            # center are weighted lowest.
            wt = (n/2)/(abs(n/2 - (i+1))+1)
            edgelength = 1/wt
            if i - n//2 + 1 != 0:
                # This edge is the furthest from the center in this loop, so we 
                # label this edge (but not the other edges in the loop)
                attr = ",".join(attr.split(",")[1:])
            # We raise the weights and edge lengths to different powers to adjust
            # the distribution of nodes across the "diameter" of the graph
            print ("    %s -- %s [weight = %f, len = %f, %s" % (
                loopli[i],loopli[i+1],wt**3,edgelength**2,attr))
    else:
        print (line)

...可以使用python adjweight.py <large.dot >large3.dot运行，其中large.dot是问题中显示的.dot文件。

将边缘的字体大小更改为10（与默认值14相比）并通过large3.dot运行neato给出了以下图表： enter image description here

这样可以在页面周围更均匀地展开节点，并且边缘标签更不易混乱，因此对我来说似乎是一种改进。边缘标签仍然与几个节点重叠（特别是在图的右上角）其他边标签，因为overlap参数仅影响节点和边线，而不影响边标签。

调整边缘的相对长度和权重，因为它们与“ECNY”的距离有关，可能有助于更多地改进图形。

Answer 2

我需要将某些节点的所有路径可视化到其他节点，所以我关于可视化节点的问题不在于简化。好吧你已经可视化从某个节点到其他节点的所有路径但是你说的AS结果是不可读的并且包含重叠**。我认为你需要的是使这个可视化清晰我的下一个建议是尝试使用（3维图形）手动对小数据进行图形化，然后对其进行编码或尝试查找属性以便在3d上将其可视化，如果它在小数据上运行良好。

我只是想着如何帮助解决这个问题所以这一切只是建议和开放式问题，以便您在问题上找到答案。

Answer 3

为什么你不首先尝试减少图表上的数据（即：即使你得到了实现 - 很难遵循图表），在图表上表示数据的主要思想是简化你将失去它）所以我的建议尝试减少你的图形数据。

Answer 4

我建议使用具有流行的力导向布局算法（http://en.wikipedia.org/wiki/Force-directed_graph_drawing）的软件/框架，其中最受欢迎的是Fruchterman-Reingold和Kamada-Kawai。我已将R（http://www.r-project.org/）与igraph package一起使用。我知道这有点不回答你的问题，因为你说你使用的是graphvis，但使用强制导向算法是最好的选择。

使用graphviz绘制大图的最佳方法是什么？

4 个答案: