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diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metric comparison preperation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import readCSV as reader\n",
+    "import glob\n",
+    "import random \n",
+    "from sklearn.manifold import MDS\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculateKSMatrix(dists):\n",
+    "    dist = []\n",
+    "\n",
+    "    for i in range(len(dists)):\n",
+    "        dist = dist + dists[i]\n",
+    "    matrix = np.empty((len(dist),len(dist)))\n",
+    "\n",
+    "    for i in range(len(dist)):\n",
+    "        matrix[i,i] = 0\n",
+    "        for j in range(i+1, len(dist)):\n",
+    "            value, p = stats.ks_2samp(dist[i], dist[j])\n",
+    "            matrix[i, j] = value\n",
+    "            matrix[j, i] = value\n",
+    "            value, p = stats.ks_2samp(dist[j], dist[i])\n",
+    "    return matrix\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculateMDS(dissimilarities):\n",
+    "    embedding = MDS(n_components=2, dissimilarity='precomputed')\n",
+    "    trans = embedding.fit_transform(X=dissimilarities)\n",
+    "    return trans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot(names, coords, index = 0, title=''):\n",
+    "    half_length = int(coords.shape[0] / len(names))\n",
+    "    color = ['blue', 'red', 'green']\n",
+    "    graph = plt.figure(index)\n",
+    "    plt.title(title)\n",
+    "    for i in range(len(names)):\n",
+    "        x = (coords[(i*half_length):((i+1)*half_length), 0].tolist())\n",
+    "        y = (coords[(i*half_length):((i+1)*half_length), 1].tolist())\n",
+    "        plt.plot(x, y, color=color[i], marker='o', label = names[i], linestyle='', alpha=0.7)\n",
+    "    plt.legend(loc='upper right')\n",
+    "    plt.savefig(fname = title+'.png', dpi=150)\n",
+    "    #graph.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Files\n",
+    "1. define class for metric reading of each graph type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GraphType:\n",
+    "    \n",
+    "    # init with path contrain files and number of files to read reader is imported from (readCSV)\n",
+    "    def __init__(self, path, number):\n",
+    "        self.out_ds = []\n",
+    "        self.nas = []\n",
+    "        self.mpcs = []\n",
+    "        models = reader.readmultiplefiles(path, number)\n",
+    "        for i in range(len(models)):\n",
+    "            out_d, na, mpc = reader.getmetrics(models[i])\n",
+    "            self.out_ds.append(out_d)\n",
+    "            self.nas.append(na)\n",
+    "            self.mpcs.append(mpc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. read metrics for each graph type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "human = GraphType('../statistics/humanOutput/', 300)\n",
+    "viatra30 = GraphType('../statistics/viatraOutput30/', 300)\n",
+    "viatra100 = GraphType('../statistics/viatraOutput100/', 300)\n",
+    "random = GraphType('../statistics/randomOutput/', 300)\n",
+    "alloy = GraphType('../statistics/alloyOutput/', 300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* outdegree comparison for human, Viatra30, and alloy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-5692e29d4679>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mout_d_coords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculateMDS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalculateKSMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mviatra30\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malloy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhuman\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Viatra (30 nodes)'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Alloy (30 nodes)'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Human'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_d_coords\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Out Degree'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-3-37c4264e6073>\u001b[0m in \u001b[0;36mcalculateKSMatrix\u001b[1;34m(dists)\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m             \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mks_2samp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py\u001b[0m in \u001b[0;36mks_2samp\u001b[1;34m(data1, data2)\u001b[0m\n\u001b[0;32m   4854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4855\u001b[0m     \"\"\"\n\u001b[1;32m-> 4856\u001b[1;33m     \u001b[0mdata1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4857\u001b[0m     \u001b[0mdata2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4858\u001b[0m     \u001b[0mn1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m    932\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 934\u001b[1;33m     \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    935\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "out_d_coords = calculateMDS(calculateKSMatrix([viatra30.out_ds, alloy.out_ds, human.out_ds]))\n",
+    "plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Out Degree')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* outdegree comparison for human, Viatra30, and alloy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_d_coords = calculateMDS(calculateKSMatrix([viatra30.nas, alloy.nas, human.nas]))\n",
+    "plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Node Activity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}