diff --git a/ExploreVis.ipynb b/ExploreVis.ipynb
new file mode 100644
index 0000000..12f0079
--- /dev/null
+++ b/ExploreVis.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import networkx as nx\n",
+    "from sklearn.manifold import TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "inputEdge = \"graph/karate.edgelist\"\n",
+    "G = nx.read_edgelist(inputEdge, nodetype=int, create_using=nx.DiGraph())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<networkx.classes.digraph.DiGraph at 0x1118db990>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def loadEmbedding(file_name):\n",
+    "    with open(file_name, 'r') as f:\n",
+    "        n, d = f.readline().strip().split()\n",
+    "        X = np.zeros((int(n)+1, int(d)))\n",
+    "        for line in f:\n",
+    "            emb = line.strip().split()\n",
+    "            emb_fl = [float(emb_i) for emb_i in emb[1:]]\n",
+    "            X[int(emb[0]),:] = emb_fl\n",
+    "    return X\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_embedding2D(node_pos, node_colors=None, di_graph=None):\n",
+    "    node_num, embedding_dimension = node_pos.shape\n",
+    "    if(embedding_dimension > 2):\n",
+    "        print \"Embedding dimensiion greater than 2, use tSNE to reduce it to 2\"\n",
+    "        model = TSNE(n_components=2)\n",
+    "        node_pos = model.fit_transform(node_pos)\n",
+    "\n",
+    "    if di_graph is None:\n",
+    "        # plot using plt scatter\n",
+    "        plt.scatter(node_pos[:,0], node_pos[:,1], c=node_colors)\n",
+    "    else:\n",
+    "        # plot using networkx with edge structure\n",
+    "        pos = {}\n",
+    "        for i in xrange(node_num):\n",
+    "            pos[i] = node_pos[i, :]\n",
+    "        if node_colors:\n",
+    "            nx.draw_networkx_nodes(di_graph, pos, node_color=node_colors, width=0.1, node_size=100, arrows=False, alpha=0.8, font_size=5)\n",
+    "        else:\n",
+    "            nx.draw_networkx(di_graph, pos, node_color=node_colors, width=0.1, node_size=300, arrows=False, alpha=0.8, font_size=12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb = loadEmbedding(\"emb/karate-2.emb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.      ,  0.      ],\n",
+       "       [ 0.647954, -1.221835],\n",
+       "       [ 0.26078 , -1.18105 ],\n",
+       "       [ 0.014604, -1.177134],\n",
+       "       [ 0.341933, -1.163942],\n",
+       "       [ 0.87369 , -1.255122],\n",
+       "       [ 0.987939, -1.255225],\n",
+       "       [ 0.891888, -1.221276],\n",
+       "       [ 0.2124  , -1.138424],\n",
+       "       [-0.105782, -1.146163],\n",
+       "       [ 0.078162, -1.200563],\n",
+       "       [ 0.660968, -1.199685],\n",
+       "       [ 0.194901, -1.163361],\n",
+       "       [ 0.332712, -1.186467],\n",
+       "       [ 0.053062, -1.161349],\n",
+       "       [-0.690653, -1.180797],\n",
+       "       [-0.404271, -1.148828],\n",
+       "       [ 0.921079, -1.241551],\n",
+       "       [ 0.3638  , -1.157203],\n",
+       "       [-0.510195, -1.145792],\n",
+       "       [ 0.01432 , -1.16636 ],\n",
+       "       [-0.726022, -1.197924],\n",
+       "       [ 0.443489, -1.180293],\n",
+       "       [-0.515541, -1.187338],\n",
+       "       [-0.492621, -1.158493],\n",
+       "       [-0.390101, -1.133154],\n",
+       "       [-0.411404, -1.129306],\n",
+       "       [-0.472089, -1.153028],\n",
+       "       [-0.487534, -1.146644],\n",
+       "       [-0.197616, -1.183069],\n",
+       "       [-0.334453, -1.14833 ],\n",
+       "       [-0.183227, -1.114288],\n",
+       "       [-0.457681, -1.146089],\n",
+       "       [-0.289914, -1.156211],\n",
+       "       [-0.674016, -1.160691]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "emb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plot_embedding2D(emb,node_colors=None,di_graph=G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VK1bwi2cO0JsuUrBdyqkQecvFsRx6kgV6UgXC/ia8tc307X6J2PxV9O35I8Iw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\nEAShhP8P7mTIl8xXGNIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1118db510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "emb_dfs = loadEmbedding(\"emb/karate-2-0.2.emb\")\n",
+    "plot_embedding2D(emb_dfs,node_colors=None,di_graph=G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jCcHOee3mMboQdMSCTNRafqMZTeB5konHbqa1bRXSsdFTM4j2LUYTgtyZ15A6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\n0gCglFJblAYApZTaojQAKKXUFvX/AXxu6cke6cOtAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x111d78a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb_bfs = loadEmbedding(\"emb/karate-2-10.emb\")\n",
+    "plot_embedding2D(emb_bfs,node_colors=None,di_graph=G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A2fM8BgcHK+8ZjTeM7ieXy5HJZOjp6SEajY4b+xrdtmPHDhzHqbipdvR6IAaw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\nGHMJi8VidHV1FdZM8oVr+cmRMXb/6jDjnlAb7qW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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1120dc050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
index a6c4e85..5b88bc2 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 # node2vec
 
+modified
+
 This repository provides a reference implementation of *node2vec* as described in the paper:<br>
 > node2vec: Scalable Feature Learning for Networks.<br>
 > Aditya Grover and Jure Leskovec.<br>
diff --git a/emb/karate.emb b/emb/karate.emb
index 8f7edb4..bdbc84b 100644
--- a/emb/karate.emb
+++ b/emb/karate.emb
@@ -1,35 +1,35 @@
-34 128
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diff --git a/emb/lesmis-homo.emb b/emb/lesmis-homo.emb
new file mode 100644
index 0000000..434a8b0
--- /dev/null
+++ b/emb/lesmis-homo.emb
@@ -0,0 +1,78 @@
+77 16
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+Javert 0.178163 -0.120896 0.349237 -0.244644 0.110469 0.008374 0.124002 0.018152 -0.461758 -0.327403 0.026528 -0.183318 0.649543 -0.151823 -0.354764 -0.352492
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+MotherInnocent 0.245316 -0.086622 0.345174 -0.543205 0.195434 -0.222074 0.049641 0.416948 -0.562388 -0.330310 -0.010935 -0.340069 0.584489 -0.167510 -0.569178 -0.150302
+Child2 0.189351 0.156231 0.366951 0.141434 0.187808 0.585066 -0.030973 -0.354893 -0.428590 -0.273213 -0.114192 -0.391208 0.930505 -0.228262 0.281023 -0.964980
+Marguerite 0.055419 -0.030012 0.507538 -0.203095 0.089611 -0.264513 -0.028762 -0.109245 -0.231126 -0.587127 -0.033135 -0.397809 0.503307 -0.484659 -0.496449 -0.334805
+Child1 0.167384 0.145576 0.335081 0.117550 0.113662 0.520202 -0.073810 -0.281777 -0.373860 -0.308681 -0.085866 -0.357229 0.880359 -0.266966 0.177758 -0.943939
+Perpetue -0.044422 -0.193358 0.551006 -0.223410 -0.020235 -0.294413 -0.059855 -0.197547 -0.221146 -0.538389 -0.071679 -0.284114 0.394227 -0.615669 -0.707280 -0.376789
+Champtercier 0.654411 -1.035699 0.557185 -0.248315 0.708796 -0.299756 -0.336097 -0.469263 -0.777955 0.177366 0.124306 0.496121 0.945765 -0.171641 -0.708445 0.482557
+Count 0.627269 -1.094204 0.534154 -0.280740 0.709694 -0.400527 -0.288545 -0.446393 -0.767751 0.163122 0.148423 0.576569 0.951780 -0.050345 -0.662971 0.545107
+MotherPlutarch 0.425864 -0.037281 0.285602 0.011549 0.293055 0.487263 -0.016974 -0.420675 -0.461670 -0.177966 0.037882 -0.174901 0.778110 -0.056039 0.106770 -0.529395
+OldMan 0.640040 -1.029875 0.493889 -0.250671 0.687813 -0.262233 -0.300003 -0.375804 -0.772535 0.154066 0.153781 0.520549 0.953317 -0.097258 -0.631965 0.445142
+MlleVaubois 0.519071 -0.131736 0.211610 -0.014370 0.064218 0.291232 -0.361265 0.277900 -0.635518 -0.191175 -0.123928 -0.043675 0.765953 -0.616961 0.101537 0.016900
+CountessDeLo 0.620514 -0.989894 0.509128 -0.239031 0.726881 -0.330422 -0.294082 -0.434725 -0.713096 0.171130 0.179516 0.544049 0.861638 -0.001290 -0.694564 0.496924
+Jondrette 0.207481 0.270738 0.351321 0.015657 0.248666 0.753330 -0.062735 0.042297 -0.638110 -0.104437 -0.245679 -0.246664 1.120565 -0.261680 0.289034 -1.004364
+Napoleon 0.665361 -1.078762 0.543386 -0.260198 0.741002 -0.321761 -0.283290 -0.489154 -0.849774 0.182082 0.138385 0.565102 1.019765 -0.036154 -0.703502 0.465658
+Gribier 0.237265 -0.203679 0.277404 -0.598729 0.251402 -0.224489 -0.018617 0.398842 -0.544612 -0.265990 0.053003 -0.265365 0.644842 -0.086259 -0.595536 -0.100346
+Geborand 0.682676 -1.032944 0.544840 -0.264461 0.717819 -0.306392 -0.289494 -0.360190 -0.806040 0.163661 0.157818 0.520406 0.954278 -0.096786 -0.705295 0.493406
+Gervais 0.183733 -0.188658 0.342214 -0.249608 0.149560 -0.050671 0.052906 0.014411 -0.467021 -0.310465 0.015583 -0.102415 0.633548 -0.197591 -0.317616 -0.189839
+Cravatte 0.629831 -1.091024 0.550768 -0.316862 0.759212 -0.371159 -0.292582 -0.403709 -0.808684 0.114420 0.148491 0.531351 1.030503 -0.085396 -0.738654 0.516080
+Labarre 0.190389 -0.143353 0.316960 -0.367529 0.146816 -0.149561 0.070639 0.185157 -0.445147 -0.419760 -0.001993 -0.169803 0.626144 -0.242036 -0.389773 -0.119632
+Scaufflaire 0.135085 -0.044430 0.370358 -0.314423 0.228424 -0.175693 0.145767 -0.000387 -0.419411 -0.485144 0.046466 -0.221018 0.590339 -0.188285 -0.335936 -0.184110
+MmeDeR 0.209987 -0.198905 0.420094 -0.327285 0.217736 -0.333390 0.018474 -0.001578 -0.394103 -0.437604 0.038050 -0.121240 0.569869 -0.311240 -0.472865 -0.032508
+Boulatruelle 0.225093 -0.154233 0.334628 -0.068818 0.043646 0.197266 -0.053638 -0.112425 -0.412498 -0.267071 -0.067410 -0.171301 0.704619 -0.277634 -0.148100 -0.408667
+Isabeau 0.218333 -0.168764 0.310477 -0.298390 0.194692 -0.120550 0.099062 0.075651 -0.485400 -0.352852 0.056089 -0.165506 0.685987 -0.188837 -0.342211 -0.125205
diff --git a/emb/lesmis-role.emb b/emb/lesmis-role.emb
new file mode 100644
index 0000000..bc2aca8
--- /dev/null
+++ b/emb/lesmis-role.emb
@@ -0,0 +1,78 @@
+77 16
+Valjean -0.129925 -0.215171 0.510442 -0.272727 -0.193650 -0.131394 0.356559 -0.049976 -0.563673 -0.610130 -0.084538 -0.190018 -0.087720 0.029813 -0.334479 -0.432589
+Gavroche -0.174787 0.010107 0.385191 0.421803 -0.121071 0.037697 0.268877 -0.657542 -0.105291 -0.791496 0.072466 -0.075712 0.059057 0.019053 0.042064 -0.782521
+Marius 0.246650 0.048983 0.404002 0.195892 -0.363448 0.325677 0.094054 -0.766824 0.022076 -0.941512 0.036916 -0.301298 0.158987 -0.342975 0.267717 -0.495259
+Javert -0.371190 -0.033745 0.277237 -0.180906 -0.409497 -0.054038 0.219860 -0.188334 -0.318100 -0.639644 -0.100939 -0.478430 -0.085044 -0.089076 -0.178185 -0.754840
+Enjolras 0.149847 0.015466 0.505411 0.333809 -0.220728 0.016008 0.152593 -0.989931 0.086197 -0.720132 0.126361 -0.000711 0.181672 -0.091381 0.005162 -0.625023
+Thenardier -0.489955 -0.203862 0.094686 0.328869 -0.420700 0.152065 0.024946 -0.417118 -0.206407 -0.798965 -0.260245 -0.523550 -0.048162 -0.119612 -0.090621 -0.795259
+Fantine -0.542523 0.273516 0.409648 -0.429880 -0.384750 -0.601903 -0.059953 -0.404373 -0.140513 -0.579073 -0.528684 -0.542092 0.138208 -0.822923 -0.404968 -0.139947
+Bossuet 0.314942 0.068010 0.514018 0.191266 -0.231852 0.083968 0.220849 -1.038469 0.071984 -0.846954 0.181227 -0.048650 0.212906 -0.039276 0.127239 -0.562932
+Courfeyrac 0.184778 0.017533 0.531614 0.237934 -0.257832 0.114875 0.200616 -0.948633 0.057604 -0.805860 0.195488 -0.094162 0.150401 -0.040746 0.049818 -0.747507
+Bahorel 0.090705 0.039634 0.541208 0.446170 -0.003965 0.139469 0.093862 -1.083703 0.053306 -0.756354 0.120078 0.137365 0.231545 -0.219300 -0.037690 -0.622298
+Joly 0.164521 0.062740 0.433189 0.348107 -0.208259 0.135672 0.086104 -1.094135 0.212176 -0.779606 0.049278 -0.074861 0.248047 -0.203288 -0.004214 -0.594419
+Combeferre 0.292440 0.154173 0.555555 0.348521 -0.186959 0.082514 0.218913 -1.052300 0.091164 -0.927594 0.177994 -0.061754 0.213517 -0.138208 0.217111 -0.621011
+Feuilly 0.218246 -0.115782 0.488836 0.247141 -0.283282 0.067810 0.181281 -0.977788 0.049556 -0.787355 0.199063 -0.117388 0.143848 0.053716 -0.048402 -0.603402
+Myriel 0.352899 -0.782162 1.053375 -0.291152 0.058157 -0.060685 0.237772 -0.350913 -0.870514 -0.319033 -0.286535 0.460125 0.004017 -0.062689 -1.142419 -0.056365
+Mabeuf 0.133854 0.051409 0.465216 0.332017 -0.217457 0.156683 0.160796 -0.923011 0.041583 -0.852048 0.144371 -0.169874 0.131911 -0.054698 0.100788 -0.677076
+Cosette -0.023499 -0.086518 0.323327 -0.150362 -0.387751 0.371755 0.101950 -0.114574 -0.351820 -0.712494 -0.164606 -0.302533 0.018620 -0.504927 -0.029771 -0.474050
+MmeThenardier -0.487307 -0.229385 0.141171 -0.035614 -0.464100 0.075426 0.097486 -0.153115 -0.328753 -0.653675 -0.310973 -0.526773 -0.096543 -0.237625 -0.327487 -0.669970
+Grantaire 0.254931 -0.058271 0.513632 0.294638 -0.177106 0.200744 0.126630 -1.071535 0.067417 -0.766632 0.147646 -0.001364 0.190783 -0.118245 -0.005483 -0.627212
+Claquesous -0.555655 -0.355935 0.149448 0.170192 -0.356517 0.055446 0.120241 -0.256516 -0.384886 -0.670667 -0.204642 -0.441994 -0.144938 0.041151 -0.323746 -0.845194
+Gueulemer -0.378836 -0.335909 0.172333 0.193847 -0.372193 0.214296 0.111468 -0.365666 -0.305985 -0.646334 -0.150867 -0.295416 -0.090082 0.032104 -0.238026 -0.918749
+Eponine -0.339924 -0.356147 0.139434 0.311152 -0.371273 0.210833 0.204454 -0.393412 -0.285592 -0.760378 -0.111116 -0.318455 -0.101038 0.110464 -0.218623 -0.875876
+Babet -0.442381 -0.315768 0.179336 0.172366 -0.331157 0.119850 0.082571 -0.273101 -0.291272 -0.576323 -0.195811 -0.374974 -0.049304 -0.040504 -0.325612 -0.762174
+Prouvaire 0.144925 0.077246 0.503540 0.451046 -0.208779 0.126210 0.150036 -1.111944 0.122711 -0.841853 0.198077 -0.025360 0.182818 -0.142734 0.064193 -0.794534
+Tholomyes -0.495772 0.407143 0.395519 -0.187898 -0.434275 -0.569660 -0.192607 -0.767430 0.084878 -0.651243 -0.647854 -0.622755 0.310192 -1.072429 -0.357389 -0.018582
+Montparnasse -0.456964 -0.290661 0.225499 0.222146 -0.321227 0.132954 0.092060 -0.269012 -0.338580 -0.665689 -0.180023 -0.376066 -0.129152 0.019295 -0.214807 -0.790166
+Bamatabois -0.228617 0.075000 0.716978 -0.322847 -0.123466 -0.483125 0.491754 0.034120 -0.627649 -0.712463 -0.063534 -0.174047 -0.179510 0.053182 -0.131603 -0.254291
+Dahlia -0.611308 0.432237 0.469521 -0.285349 -0.398037 -0.649376 -0.201578 -0.779706 0.026839 -0.746188 -0.765991 -0.672469 0.288734 -1.220406 -0.484507 0.007109
+Blacheville -0.613578 0.394695 0.496790 -0.299874 -0.405059 -0.644715 -0.247484 -0.685706 0.018003 -0.672033 -0.753292 -0.577958 0.299620 -1.177371 -0.589728 0.046095
+Listolier -0.615203 0.389135 0.509110 -0.302507 -0.332396 -0.701289 -0.215091 -0.746368 -0.004872 -0.624036 -0.792224 -0.615274 0.263089 -1.170361 -0.631824 0.102218
+Zephine -0.606614 0.249279 0.438181 -0.286901 -0.350521 -0.517839 -0.287636 -0.552277 -0.040210 -0.671894 -0.767049 -0.611427 0.230630 -1.196762 -0.603351 -0.081609
+Favourite -0.582809 0.387159 0.473431 -0.294515 -0.398514 -0.569755 -0.203097 -0.578860 -0.020023 -0.702451 -0.673514 -0.608653 0.234734 -1.074837 -0.438201 -0.023030
+Fameuil -0.518546 0.387303 0.435233 -0.315025 -0.362707 -0.621959 -0.127569 -0.585738 -0.047847 -0.663284 -0.581481 -0.612166 0.222172 -1.022039 -0.385825 -0.058191
+Brujon -0.610006 -0.333356 0.120581 0.502271 -0.337829 0.234920 0.094147 -0.389056 -0.259785 -0.736650 -0.202879 -0.349471 -0.081087 0.002155 -0.215799 -1.081527
+MmeHucheloup 0.004005 0.095351 0.404157 0.338441 -0.254499 0.169371 0.053296 -0.950988 0.111278 -0.838795 0.096218 -0.206954 0.130868 -0.202705 0.122365 -0.775840
+Cochepaille -0.214299 0.114431 0.832271 -0.356587 -0.088740 -0.634992 0.609369 -0.019214 -0.682755 -0.811510 0.036798 -0.174917 -0.243498 0.291296 -0.059764 -0.340748
+MlleGillenormand 0.429257 -0.187992 0.473544 -0.239425 -0.374585 0.594764 0.022109 -0.117192 -0.319377 -0.918906 -0.136339 -0.224527 0.091580 -0.636522 0.173786 -0.158268
+Chenildieu -0.199067 0.126918 0.882789 -0.444507 -0.073614 -0.620070 0.607297 0.130394 -0.750944 -0.872104 -0.054263 -0.133701 -0.222617 0.008021 0.007117 -0.278201
+Champmathieu -0.118483 0.025567 0.851256 -0.433422 -0.090320 -0.452450 0.565244 0.133641 -0.769406 -0.807042 -0.061220 -0.102585 -0.214722 0.088226 -0.086612 -0.300400
+Brevet -0.213736 0.186103 0.993713 -0.407695 -0.011577 -0.755159 0.730045 0.075189 -0.808654 -0.939249 -0.022850 -0.069787 -0.279937 0.111626 -0.032132 -0.305275
+Judge -0.247542 0.163980 0.798037 -0.308416 -0.102600 -0.626868 0.684136 0.031700 -0.722012 -0.954613 0.025970 -0.197488 -0.236881 0.232348 0.115457 -0.298857
+Gillenormand 0.157674 -0.091841 0.329446 -0.336615 -0.325369 0.472920 0.128635 -0.048598 -0.327737 -0.772861 -0.100948 -0.303915 0.150008 -0.702926 -0.073874 -0.464607
+Fauchelevent -0.116067 -0.182406 0.566032 -0.709211 -0.312033 -0.198472 0.175639 0.069991 -0.614576 -0.628020 -0.220222 -0.583703 -0.301354 0.231837 -0.306512 -0.535745
+LtGillenormand 0.302191 -0.162549 0.313488 -0.270372 -0.456255 0.691636 0.007722 -0.036953 -0.345403 -0.894789 -0.200583 -0.435729 0.123665 -0.772115 0.004470 -0.342186
+Simplice -0.378701 0.147999 0.444617 -0.638876 -0.413484 -0.531707 0.152304 -0.079948 -0.360967 -0.577197 -0.356433 -0.477277 0.006806 -0.444950 -0.229552 -0.309578
+MlleBaptistine 0.099359 -0.610441 0.844536 -0.222336 0.026567 -0.055812 0.203490 -0.230326 -0.788858 -0.328029 -0.288046 0.217258 -0.068692 -0.056578 -0.980277 -0.202749
+MmeMagloire 0.181406 -0.645879 0.864151 -0.225080 -0.025212 0.024223 0.227343 -0.270425 -0.713304 -0.327661 -0.278010 0.251070 -0.056758 -0.092394 -0.921928 -0.174281
+Woman2 -0.103467 -0.142765 0.414315 -0.228745 -0.293957 0.100625 0.177943 -0.115962 -0.392013 -0.568169 -0.136840 -0.267726 -0.024628 -0.255566 -0.219344 -0.473703
+Toussaint -0.243851 -0.102728 0.431137 -0.287532 -0.302487 -0.047558 0.244548 -0.087105 -0.432109 -0.553336 -0.158080 -0.256090 -0.047934 -0.219362 -0.276095 -0.595179
+Pontmercy 0.239390 -0.405762 0.303583 0.027737 -0.495470 0.508315 -0.009373 -0.137872 -0.481324 -1.092692 -0.228456 -0.586858 -0.053010 -0.294855 0.281182 -0.183644
+Anzelma -0.457653 -0.292069 0.176086 0.245435 -0.400119 0.109571 0.126430 -0.361040 -0.241185 -0.696343 -0.139829 -0.474394 -0.100200 0.017039 -0.256120 -0.830916
+MotherInnocent -0.157798 -0.149275 0.429188 -0.470676 -0.287391 -0.068667 0.118859 -0.014497 -0.482078 -0.596091 -0.276682 -0.541793 -0.194253 0.012940 -0.299101 -0.496778
+Woman1 -0.385351 -0.118148 0.410497 -0.414237 -0.315156 -0.276001 0.145469 -0.059019 -0.504404 -0.575254 -0.303252 -0.366098 -0.080929 -0.197441 -0.332611 -0.441794
+MmeBurgon -0.501113 0.336340 0.685064 0.944209 0.593426 0.540230 0.038338 -0.545280 -0.415502 -0.981272 0.092690 0.550945 0.034698 -0.893310 0.223277 -1.109576
+Perpetue -0.580357 0.198920 0.427267 -0.665197 -0.440386 -0.653142 0.044648 -0.210912 -0.341010 -0.524991 -0.458112 -0.524484 0.025299 -0.648082 -0.374461 -0.331263
+Marguerite -0.205280 -0.051224 0.531266 -0.367198 -0.226668 -0.359407 0.112800 -0.182801 -0.424047 -0.490804 -0.333093 -0.277853 0.015055 -0.324723 -0.433138 -0.182381
+Child2 -0.230254 0.018172 0.576231 0.616542 0.031309 0.040857 0.315639 -0.670054 -0.170486 -0.893997 0.168628 0.060358 0.053993 -0.007670 0.216826 -1.065923
+Child1 -0.214432 -0.061901 0.536501 0.574151 -0.019596 0.105876 0.279741 -0.576045 -0.233108 -0.862613 0.156414 0.133027 -0.042376 0.066584 0.102198 -1.094561
+BaronessT 0.140515 0.039135 0.363074 -0.074272 -0.330229 0.407959 0.032805 -0.327154 -0.201993 -0.805412 -0.074725 -0.289029 0.145635 -0.608271 0.028261 -0.497620
+MmePontmercy 0.531256 -0.388923 0.404661 -0.167075 -0.547897 0.761587 0.061288 0.009116 -0.640711 -1.296472 -0.201408 -0.542035 -0.026666 -0.390813 0.502390 -0.111086
+Magnon -0.165956 -0.243802 0.181337 -0.190585 -0.378044 0.322372 0.121724 -0.133380 -0.312873 -0.660703 -0.151861 -0.420277 0.053064 -0.485795 -0.339272 -0.641101
+CountessDeLo 0.446093 -0.800811 1.180025 -0.289185 0.102130 -0.039002 0.163817 -0.558980 -0.800171 -0.350723 -0.333419 0.436979 0.099624 -0.116268 -1.370710 -0.010257
+Napoleon 0.437214 -0.821518 1.167139 -0.356247 0.043016 -0.005225 0.127822 -0.509737 -0.863110 -0.326408 -0.460652 0.426726 0.103526 -0.275756 -1.369353 0.027258
+Labarre -0.080393 -0.219485 0.576315 -0.319672 -0.179815 -0.104536 0.275462 -0.101944 -0.529537 -0.614834 -0.225092 -0.136256 -0.064892 -0.085516 -0.369477 -0.294143
+Champtercier 0.266213 -0.840920 1.048270 -0.340334 0.044517 0.006789 0.159510 -0.360194 -0.870343 -0.287764 -0.445982 0.314507 0.029963 -0.195029 -1.381412 -0.068862
+Cravatte 0.369188 -0.888838 1.193980 -0.326128 0.125248 0.030991 0.215950 -0.378020 -0.907069 -0.414180 -0.361891 0.456643 0.047713 -0.133073 -1.342715 -0.127161
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+Geborand 0.381058 -0.809070 1.101842 -0.327159 0.085080 -0.036664 0.186910 -0.366111 -0.834514 -0.299315 -0.380339 0.368575 0.042121 -0.120412 -1.280308 -0.017520
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+MotherPlutarch 0.125005 -0.078138 0.421320 0.374888 -0.201180 0.356100 0.118636 -0.874878 -0.031980 -0.849892 0.097992 -0.123060 0.125037 -0.134964 0.057364 -0.802485
diff --git a/graph/lesmis.gml b/graph/lesmis.gml
new file mode 100644
index 0000000..3c07511
--- /dev/null
+++ b/graph/lesmis.gml
@@ -0,0 +1,1913 @@
+Creator "Mark Newman on Fri Jul 21 12:44:53 2006"
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diff --git a/src/emb_cluster.py b/src/emb_cluster.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/main.py b/src/main.py
index 82ac735..f6467ec 100644
--- a/src/main.py
+++ b/src/main.py
@@ -67,6 +67,12 @@ def read_graph():
 	'''
 	Reads the input network in networkx.
 	'''
+	if args.input.split('.')[-1] == 'gml':
+		G = nx.read_gml(args.input)
+		for edge in G.edges():
+			G[edge[0]][edge[1]]['weight'] = 1
+		return G
+
 	if args.weighted:
 		G = nx.read_edgelist(args.input, nodetype=int, data=(('weight',float),), create_using=nx.DiGraph())
 	else:
@@ -85,7 +91,7 @@ def learn_embeddings(walks):
 	'''
 	walks = [map(str, walk) for walk in walks]
 	model = Word2Vec(walks, size=args.dimensions, window=args.window_size, min_count=0, sg=1, workers=args.workers, iter=args.iter)
-	model.save_word2vec_format(args.output)
+	model.wv.save_word2vec_format(args.output)
 	
 	return
 
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000..5a0a10b
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,60 @@
+import matplotlib.pyplot as plt 
+import networkx as nx
+import numpy as np
+from sklearn.manifold import TSNE
+
+## Copy from https://github.com/palash1992/GEM/blob/master/gem/evaluation/visualize_embedding.py
+
+def plot_embedding(node_pos, node_colors=None, di_graph=None):
+    node_num, embedding_dimension = node_pos.shape
+    if(embedding_dimension > 2):
+        print "Embedding dimensiion greater than 2, use tSNE to reduce it to 2"
+        model = TSNE(n_components=2)
+        node_pos = model.fit_transform(node_pos)
+
+    if di_graph is None:
+        # plot using plt scatter
+        plt.scatter(node_pos[:,0], node_pos[:,1], c=node_colors)
+    else:
+        # plot using networkx with edge structure
+        pos = {}
+        for i in xrange(node_num):
+            pos[i] = node_pos[i, :]
+        if node_colors:
+            nx.draw_networkx_nodes(di_graph, pos, node_color=node_colors, width=0.1, node_size=100, arrows=False, alpha=0.8, font_size=5)
+        else:
+            nx.draw_networkx(di_graph, pos, node_color=node_colors, width=0.1, node_size=300, arrows=False, alpha=0.8, font_size=12)
+    plt.show()
+
+def read_graph(graph_path,weighted,directed):
+    '''
+    Reads the input network in networkx.
+    '''
+    if graph_path.split('.')[-1] == 'gml':
+        G = nx.read_gml(graph_path)
+        for edge in G.edges():
+            G[edge[0]][edge[1]]['weight'] = 1
+        return G
+
+    if weighted:
+        G = nx.read_edgelist(graph_path, nodetype=int, data=(('weight',float),), create_using=nx.DiGraph())
+    else:
+        G = nx.read_edgelist(graph_path, nodetype=int, create_using=nx.DiGraph())
+        for edge in G.edges():
+            G[edge[0]][edge[1]]['weight'] = 1
+
+    if not directed:
+        G = G.to_undirected()
+
+    return G
+
+def load_embedding(file_name):
+    with open(file_name, 'r') as f:
+        n, d = f.readline().strip().split()
+        X = np.zeros((int(n)+1, int(d)))
+        for line in f:
+            emb = line.strip().split()
+            emb_fl = [float(emb_i) for emb_i in emb[1:]]
+            X[int(emb[0]),:] = emb_fl
+    return X
+
diff --git a/src/vis_emb.py b/src/vis_emb.py
new file mode 100644
index 0000000..a53c52a
--- /dev/null
+++ b/src/vis_emb.py
@@ -0,0 +1,33 @@
+from utils import read_graph, load_embedding, plot_embedding
+import argparse
+
+def parse_args():
+	'''
+	Parses the node2vec arguments.
+	'''
+	parser = argparse.ArgumentParser(description="Run node2vec.")
+
+	parser.add_argument('--graph', nargs='?', default='graph/karate.edgelist',
+	                    help='Input graph path')
+
+	parser.add_argument('--emb', nargs='?', default='emb/karate.emb', help='Input emb path')
+	parser.add_argument('--weighted', dest='weighted', action='store_true',
+	                    help='Boolean specifying (un)weighted. Default is unweighted.')
+	parser.add_argument('--unweighted', dest='unweighted', action='store_false')
+	parser.set_defaults(weighted=False)
+
+	parser.add_argument('--directed', dest='directed', action='store_true',
+	                    help='Graph is (un)directed. Default is undirected.')
+	parser.add_argument('--undirected', dest='undirected', action='store_false')
+	parser.set_defaults(directed=False)
+	return parser.parse_args()
+
+
+def main(args):
+	G = read_graph(args.graph, args.weighted, args.directed)
+	emb = load_embedding(args.emb)
+	plot_embedding(emb,node_colors=None,di_graph=G)
+
+if __name__ == "__main__":
+	args = parse_args()
+	main(args)