covid19-graph-build.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import lit\n",
    "from functools import reduce\n",
    "from pyspark.sql import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder.getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------+--------+------+-----+--------------------+-----------+\n",
      "| Id| Article|    Tags|Design| Name|                Text|     Labels|\n",
      "+---+--------+--------+------+-----+--------------------+-----------+\n",
      "|  0|wvx6q999|COVID-19|     0|TITLE|Note from the edi...|   FRAGMENT|\n",
      "|  1|wvx6q999|COVID-19|     0| null|The situation has...|       null|\n",
      "|  2|wvx6q999|COVID-19|     0| null|Meanwhile, on 7 J...|       null|\n",
      "|  3|wvx6q999|COVID-19|     0| null|In order to suppo...|       null|\n",
      "|  4|wvx6q999|COVID-19|     0| null|For example, ther...|   FRAGMENT|\n",
      "|  5|wvx6q999|COVID-19|     0| null|In this issue of ...|   FRAGMENT|\n",
      "|  6|wvx6q999|COVID-19|     0| null|One is a research...|   FRAGMENT|\n",
      "|  7|wvx6q999|COVID-19|     0| null|on the developmen...|       null|\n",
      "|  8|wvx6q999|COVID-19|     0| null|Before this publi...|       null|\n",
      "|  9|wvx6q999|COVID-19|     0| null|The other is a ra...|       null|\n",
      "| 10|wvx6q999|COVID-19|     0| null|It also points ou...|   FRAGMENT|\n",
      "| 11|wvx6q999|COVID-19|     0| null|Today, the WHO Di...|   FRAGMENT|\n",
      "| 12|wvx6q999|COVID-19|     0| null|International hea...|       null|\n",
      "| 13|wvx6q999|COVID-19|     0| null|ECDC has set up a...|       null|\n",
      "| 14|wvx6q999|COVID-19|     0| null|At the end of 201...|       null|\n",
      "| 15|wvx6q999|COVID-19|     0| null|Just a day earlie...|       null|\n",
      "| 16|wvx6q999|COVID-19|     0| null|The post indicate...|SAMPLE_SIZE|\n",
      "| 17|wvx6q999|COVID-19|     0| null|In their accompan...|       null|\n",
      "| 18|wvx6q999|COVID-19|     0| null|severe acute resp...|   FRAGMENT|\n",
      "| 19|wvx6q999|COVID-19|     0| null|In order to suppo...|       null|\n",
      "+---+--------+--------+------+-----+--------------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+--------+------+-------------------+--------------------+--------------------+--------------------+--------+------+----+--------------------+--------------------+--------------------+----------+\n",
      "|      Id|Source|          Published|         Publication|             Authors|               Title|    Tags|Design|Size|              Sample|              Method|           Reference|     Entry|\n",
      "+--------+------+-------------------+--------------------+--------------------+--------------------+--------+------+----+--------------------+--------------------+--------------------+----------+\n",
      "|wvx6q999|   PMC|2020-01-23 00:00:00|       Euro Surveill|                null|Note from the edi...|COVID-19|     0|  20|The post indicate...|                null|https://www.ncbi....|2020-03-27|\n",
      "|64q3qcjy|   PMC|2020-02-27 00:00:00|       Euro Surveill|                null|Latest assessment...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-03-27|\n",
      "|bd7whqxf|   PMC|2020-03-05 00:00:00|       Euro Surveill|                null|Updated rapid ris...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-03-27|\n",
      "|l3ijkmwy|   PMC|2020-03-12 00:00:00|       Euro Surveill|                null|Updated rapid ris...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-03-27|\n",
      "|54glvasa|   PMC|2020-03-12 00:00:00|Med Klin Intensiv...|Kluge, Stefan; Ja...|Empfehlungen zur ...|COVID-19|     0|null|Grundsätzlich emp...|                null|https://www.ncbi....|2020-05-19|\n",
      "|t4ro1rl9|   PMC|2020-03-18 00:00:00|  Intensive Care Med|Corradi, F.; Via,...|Lung ultrasound a...|COVID-19|     0|null|The potential adv...|Semiquantitative ...|https://www.ncbi....|2020-05-19|\n",
      "|88hibsk5|   PMC|2020-03-23 00:00:00|Bone Marrow Trans...|Szer, Jeff; Weisd...|The impact of COV...|COVID-19|     0|null|The database cont...|The database cont...|https://www.ncbi....|2020-05-19|\n",
      "|8k34ed1o|   PMC|2020-03-30 00:00:00|    MMW Fortschr Med|                null|Fortbildung in Ze...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-04-03|\n",
      "|7s2i3sbj|   PMC|2020-03-30 00:00:00|    MMW Fortschr Med|                null|Schon junge Ärzte...|COVID-19|     0|null|COVID-19 spielte ...|COVID-19 spielte ...|https://www.ncbi....|2020-04-03|\n",
      "|1ob719sh|   PMC|2020-03-27 00:00:00|           Br Dent J|      Sellars, Shaun|   Interesting times|COVID-19|     0|null|Costs for alterna...|As 2020 enters fu...|https://www.ncbi....|2020-04-03|\n",
      "|cgcuzxdt|   PMC|2020-03-04 00:00:00|               Orbis| Shattuck, Thomas J.|The Race to Zero?...|COVID-19|     0|   2|The two men had p...|Between her elect...|https://www.ncbi....|2020-05-12|\n",
      "|apcueljv|   PMC|2020-03-19 00:00:00|   Lancet Psychiatry|Zhai, Yusen; Du, Xue|Mental health car...|COVID-19|     0|null|Coronavirus disea...|Coronavirus disea...|https://www.ncbi....|2020-06-04|\n",
      "|13pj0wmb|   PMC|2020-03-20 00:00:00|Lancet Public Health|Armitage, Richard...|COVID-19 and the ...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-03-27|\n",
      "|umz2v0ka|   PMC|2020-03-30 00:00:00|       Health Expect|      Aslani, Parisa|What are our heal...|COVID-19|     0|null|For weeks, we wer...|For weeks, we wer...|https://www.ncbi....|2020-04-10|\n",
      "|wuuhlowd|   PMC|2020-04-01 00:00:00|          Inter Econ|     Valkonen, Tarmo|The Finnish Pensi...|COVID-19|     9|null|Several new early...|Private sector pe...|https://www.ncbi....|2020-05-19|\n",
      "|1mchg5yg|   PMC|2020-03-09 00:00:00|                null|Zhao, Shi; Cao, P...|COVID-19 and gend...|COVID-19|     0|   8|1 The mechanism b...|We examined the p...|https://www.ncbi....|2020-04-10|\n",
      "|6dwpkjaj|   PMC|2020-03-26 00:00:00|       Euro Surveill|                null|Updated rapid ris...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-04-10|\n",
      "|qwc0inzi|   PMC|2020-03-11 00:00:00|       J Nurse Pract| Neal-Boylan, Leslie|Are We Ready for ...|COVID-19|     0|   4|I quickly moved m...|                null|https://www.ncbi....|2020-05-19|\n",
      "|cmqtarm0|   PMC|2020-03-30 00:00:00|        Lancet Oncol| The Lancet Oncology|COVID-19: global ...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-04-03|\n",
      "|vahd59z8|   PMC|2020-04-01 00:00:00|  J Am Med Dir Assoc|Cesari, Matteo; P...|COVID-19 in Italy...|COVID-19|     0|null|                null|                null|https://www.ncbi....|2020-05-19|\n",
      "+--------+------+-------------------+--------------------+--------------------+--------------------+--------+------+----+--------------------+--------------------+--------------------+----------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+--------------------+--------+\n",
      "|               Title|Mentions|\n",
      "+--------------------+--------+\n",
      "|                null|   25717|\n",
      "|Detection of 2019...|     878|\n",
      "|Real-time tentati...|       6|\n",
      "|Risk assessment: ...|       4|\n",
      "|European Centre f...|      37|\n",
      "|Real-time tentati...|      82|\n",
      "|Novel 2019 corona...|       5|\n",
      "|Undiagnosed pneum...|       1|\n",
      "|Laboratory testin...|      18|\n",
      "|Detection of 2019...|      43|\n",
      "|Covid-19 drug int...|       4|\n",
      "|Clinicalcourseand...|       5|\n",
      "|Clinical features...|    3607|\n",
      "|AWMF (2020) S3-Le...|       1|\n",
      "|Exhaled air dispe...|      57|\n",
      "|Novel coronavirus...|       5|\n",
      "|Staff safety duri...|      95|\n",
      "|AWMF (2017) S3-Le...|       2|\n",
      "|Novel coronavirus...|      12|\n",
      "|COVID-19: a novel...|      35|\n",
      "+--------------------+--------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dfs_sections = spark.read \\\n",
    ".format(\"jdbc\") \\\n",
    ".option(\"url\", \"jdbc:sqlite:/nfs/workspaces/datasets/covid-19/articles.sqlite\") \\\n",
    ".option(\"dbtable\", \"(SELECT * FROM sections)\").load()\n",
    "dfs_sections.show()\n",
    "dfs_articles = spark.read \\\n",
    ".format(\"jdbc\") \\\n",
    ".option(\"url\", \"jdbc:sqlite:/nfs/workspaces/datasets/covid-19/articles.sqlite\") \\\n",
    ".option(\"query\", \"(SELECT * FROM articles)\") \\\n",
    ".option(\"customSchema\", \"Published STRING, Entry STRING\").load()\n",
    "dfs_articles.show()\n",
    "dfs_citations = spark.read \\\n",
    ".format(\"jdbc\") \\\n",
    ".option(\"url\", \"jdbc:sqlite:/nfs/workspaces/datasets/covid-19/articles.sqlite\") \\\n",
    ".option(\"query\", \"(SELECT * FROM citations)\").load()\n",
    "dfs_citations.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+---+----------+--------+\n",
      "|              entity|  n|section_id|ner_type|\n",
      "+--------------------+---+----------+--------+\n",
      "|                AAK1|  1|      1522|   BC2GM|\n",
      "|      Januase Kinase|  1|      1523|   BC2GM|\n",
      "|              IL - 8|  1|      3461|   BC2GM|\n",
      "|              IL - 8|  2|      3461|   BC2GM|\n",
      "|              IL - 8|  3|      3461|   BC2GM|\n",
      "|                b A1|  1|      6798|   BC2GM|\n",
      "|                BafA|  2|      6798|   BC2GM|\n",
      "|                BafA|  1|      6799|   BC2GM|\n",
      "|vacuolar type H (...|  2|      6799|   BC2GM|\n",
      "|          v - ATPase|  3|      6799|   BC2GM|\n",
      "|                   C|  1|      6800|   BC2GM|\n",
      "|                BafA|  2|      6800|   BC2GM|\n",
      "|mammalian target ...|  3|      6800|   BC2GM|\n",
      "|              mTORC1|  4|      6800|   BC2GM|\n",
      "|              mTORC1|  1|      6801|   BC2GM|\n",
      "|              mTORC1|  2|      6801|   BC2GM|\n",
      "|              mTORC1|  1|      6803|   BC2GM|\n",
      "|transcription fac...|  1|      6804|   BC2GM|\n",
      "|                TFEB|  2|      6804|   BC2GM|\n",
      "|              mTORC1|  3|      6804|   BC2GM|\n",
      "+--------------------+---+----------+--------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ner_type = [\"BC2GM\",\"BC4CHEMD\",\"BC5CDR-chem\",\"BC5CDR-disease\",\"JNLPBA\",\"NCBI-disease\",\"linnaeus\",\"s800\"]\n",
    "ner_app = []\n",
    "for item in ner_type :\n",
    "    ner_path = \"/nfs/storages/bio_corpus/ner/\"+ item  + \"/all_entity.csv\"\n",
    "    df = spark.read.format(\"csv\").option(\"header\",\"true\").load(ner_path)\n",
    "    df = df.withColumn(\"type\", lit(item))\n",
    "    ner_app.append(df)\n",
    "df_ner_all = reduce(DataFrame.unionAll, ner_app)\n",
    "df_ner_all = df_ner_all.withColumnRenamed(\"type\", \"ner_type\")\n",
    "df_ner_all.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "re_type = [\"GAD\",\"euadr\"]\n",
    "re_app = []\n",
    "for item in re_type:\n",
    "    re_path = \"/nfs/storages/bio_corpus/re_all_in_one/\" + item + \"/all_relations.csv\"\n",
    "    df = spark.read.format(\"csv\").option(\"header\",\"true\").load(re_path)\n",
    "    df = df.withColumn(\"type\", lit(item))\n",
    "    re_app.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------+\n",
      "|section_id|re_type|\n",
      "+----------+-------+\n",
      "|       222|    GAD|\n",
      "|      2780|    GAD|\n",
      "|      4270|    GAD|\n",
      "|      8822|    GAD|\n",
      "|      9142|    GAD|\n",
      "|      9614|    GAD|\n",
      "|      9998|    GAD|\n",
      "|     10000|    GAD|\n",
      "|     11154|    GAD|\n",
      "|     12708|    GAD|\n",
      "|     12709|    GAD|\n",
      "|     12710|    GAD|\n",
      "|     13385|    GAD|\n",
      "|     14966|    GAD|\n",
      "|     15913|    GAD|\n",
      "|     15918|    GAD|\n",
      "|     17060|    GAD|\n",
      "|     18664|    GAD|\n",
      "|     18665|    GAD|\n",
      "|     20429|    GAD|\n",
      "+----------+-------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_re_all = reduce(DataFrame.unionAll, re_app)\n",
    "df_re_all = df_re_all.withColumnRenamed(\"type\", \"re_type\")\n",
    "df_re_all.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------------------+---+--------+-------+\n",
      "|section_id|              entity|  n|ner_type|re_type|\n",
      "+----------+--------------------+---+--------+-------+\n",
      "|      1523|      Januase Kinase|  1|   BC2GM|  euadr|\n",
      "|      6799|                BafA|  1|   BC2GM|  euadr|\n",
      "|      6799|vacuolar type H (...|  2|   BC2GM|  euadr|\n",
      "|      6799|          v - ATPase|  3|   BC2GM|  euadr|\n",
      "|      6803|              mTORC1|  1|   BC2GM|  euadr|\n",
      "|      6805|                BafA|  1|   BC2GM|  euadr|\n",
      "|      6805|              mTORC1|  2|   BC2GM|  euadr|\n",
      "|     10479|                   h|  1|   BC2GM|  euadr|\n",
      "|     16293|   ribosome helicase|  1|   BC2GM|  euadr|\n",
      "|     18261|                  CR|  1|   BC2GM|  euadr|\n",
      "|     18261|              Cas13b|  2|   BC2GM|  euadr|\n",
      "|     18265|              Cas13b|  1|   BC2GM|  euadr|\n",
      "|     20120|      HLA - B27 mRNA|  1|   BC2GM|  euadr|\n",
      "|     20122|      HLA - B27 mRNA|  1|   BC2GM|  euadr|\n",
      "|     23056|               renin|  1|   BC2GM|  euadr|\n",
      "|     23056|         angiotensin|  2|   BC2GM|  euadr|\n",
      "|     23056|                  RA|  3|   BC2GM|  euadr|\n",
      "|     23058| Angiotensin peptide|  1|   BC2GM|  euadr|\n",
      "|     23059|               renin|  1|   BC2GM|  euadr|\n",
      "|     23059|         angiotensin|  2|   BC2GM|  euadr|\n",
      "+----------+--------------------+---+--------+-------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+----------+--------------------+-----+------------+-----------+\n",
      "|section_id|          entity_src|n_src|ner_type_src|re_type_src|\n",
      "+----------+--------------------+-----+------------+-----------+\n",
      "|      1523|      Januase Kinase|    1|       BC2GM|      euadr|\n",
      "|      6799|                BafA|    1|       BC2GM|      euadr|\n",
      "|      6799|vacuolar type H (...|    2|       BC2GM|      euadr|\n",
      "|      6799|          v - ATPase|    3|       BC2GM|      euadr|\n",
      "|      6803|              mTORC1|    1|       BC2GM|      euadr|\n",
      "|      6805|                BafA|    1|       BC2GM|      euadr|\n",
      "|      6805|              mTORC1|    2|       BC2GM|      euadr|\n",
      "|     10479|                   h|    1|       BC2GM|      euadr|\n",
      "|     16293|   ribosome helicase|    1|       BC2GM|      euadr|\n",
      "|     18261|                  CR|    1|       BC2GM|      euadr|\n",
      "|     18261|              Cas13b|    2|       BC2GM|      euadr|\n",
      "|     18265|              Cas13b|    1|       BC2GM|      euadr|\n",
      "|     20120|      HLA - B27 mRNA|    1|       BC2GM|      euadr|\n",
      "|     20122|      HLA - B27 mRNA|    1|       BC2GM|      euadr|\n",
      "|     23056|               renin|    1|       BC2GM|      euadr|\n",
      "|     23056|         angiotensin|    2|       BC2GM|      euadr|\n",
      "|     23056|                  RA|    3|       BC2GM|      euadr|\n",
      "|     23058| Angiotensin peptide|    1|       BC2GM|      euadr|\n",
      "|     23059|               renin|    1|       BC2GM|      euadr|\n",
      "|     23059|         angiotensin|    2|       BC2GM|      euadr|\n",
      "+----------+--------------------+-----+------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+----------+--------------------+-----+------------+-----------+\n",
      "|section_id|          entity_dst|n_dst|ner_type_dst|re_type_dst|\n",
      "+----------+--------------------+-----+------------+-----------+\n",
      "|      1523|      Januase Kinase|    1|       BC2GM|      euadr|\n",
      "|      6799|                BafA|    1|       BC2GM|      euadr|\n",
      "|      6799|vacuolar type H (...|    2|       BC2GM|      euadr|\n",
      "|      6799|          v - ATPase|    3|       BC2GM|      euadr|\n",
      "|      6803|              mTORC1|    1|       BC2GM|      euadr|\n",
      "|      6805|                BafA|    1|       BC2GM|      euadr|\n",
      "|      6805|              mTORC1|    2|       BC2GM|      euadr|\n",
      "|     10479|                   h|    1|       BC2GM|      euadr|\n",
      "|     16293|   ribosome helicase|    1|       BC2GM|      euadr|\n",
      "|     18261|                  CR|    1|       BC2GM|      euadr|\n",
      "|     18261|              Cas13b|    2|       BC2GM|      euadr|\n",
      "|     18265|              Cas13b|    1|       BC2GM|      euadr|\n",
      "|     20120|      HLA - B27 mRNA|    1|       BC2GM|      euadr|\n",
      "|     20122|      HLA - B27 mRNA|    1|       BC2GM|      euadr|\n",
      "|     23056|               renin|    1|       BC2GM|      euadr|\n",
      "|     23056|         angiotensin|    2|       BC2GM|      euadr|\n",
      "|     23056|                  RA|    3|       BC2GM|      euadr|\n",
      "|     23058| Angiotensin peptide|    1|       BC2GM|      euadr|\n",
      "|     23059|               renin|    1|       BC2GM|      euadr|\n",
      "|     23059|         angiotensin|    2|       BC2GM|      euadr|\n",
      "+----------+--------------------+-----+------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+----------+----------+-----+--------------+-----------+-----------+-----+--------------+-----------+\n",
      "|section_id|entity_src|n_src|  ner_type_src|re_type_src| entity_dst|n_dst|  ner_type_dst|re_type_dst|\n",
      "+----------+----------+-----+--------------+-----------+-----------+-----+--------------+-----------+\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr| COVID - 19|    1|  NCBI-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr| COVID - 19|    1|  NCBI-disease|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|respiratory|    2|  NCBI-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|respiratory|    2|  NCBI-disease|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr| COVID - 19|    3|  NCBI-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr| COVID - 19|    3|  NCBI-disease|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr| COVID - 19|    1|          s800|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr| COVID - 19|    1|          s800|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|   COD - 19|    2|          s800|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|   COD - 19|    2|          s800|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|    patient|    1|      linnaeus|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|    patient|    1|      linnaeus|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|         CO|    1|BC5CDR-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|         CO|    1|BC5CDR-disease|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|respiratory|    2|BC5CDR-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|respiratory|    2|BC5CDR-disease|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|         CO|    3|BC5CDR-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|      euadr|         CO|    3|BC5CDR-disease|        GAD|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|        GAD| COVID - 19|    1|  NCBI-disease|      euadr|\n",
      "|   1000888|        CO|    1|BC5CDR-disease|        GAD| COVID - 19|    1|  NCBI-disease|        GAD|\n",
      "+----------+----------+-----+--------------+-----------+-----------+-----+--------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_ner_re = df_ner_all.join(df_re_all, \"section_id\")\n",
    "df_ner_re.show()\n",
    "df_ner_re_src = df_ner_re.withColumnRenamed(\"entity\",\"entity_src\") \\\n",
    ".withColumnRenamed(\"n\", \"n_src\") \\\n",
    ".withColumnRenamed(\"ner_type\", \"ner_type_src\") \\\n",
    ".withColumnRenamed(\"re_type\", \"re_type_src\")\n",
    "df_ner_re_src.show()\n",
    "df_ner_re_dst = df_ner_re.withColumnRenamed(\"entity\",\"entity_dst\") \\\n",
    ".withColumnRenamed(\"n\", \"n_dst\") \\\n",
    ".withColumnRenamed(\"ner_type\", \"ner_type_dst\") \\\n",
    ".withColumnRenamed(\"re_type\", \"re_type_dst\")\n",
    "df_ner_re_dst.show()\n",
    "df_ner_re_src_dst = df_ner_re_src.join(df_ner_re_dst, \"section_id\")\n",
    "df_ner_re_src_dst.show()\n",
    "df_ner_re_src_dst = df_ner_re_src_dst.filter(\"entity_src != entity_dst\") \\\n",
    ".filter(\"re_type_src == re_type_dst\") \\\n",
    ".filter(\"ner_type_src == ner_type_dst\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import col\n",
    "from pyspark.sql.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_drop_duplicate = df_ner_re_src_dst.withColumn(\"entity_pair\",array_join(array_sort(array(col(\"entity_src\"),col(\"entity_dst\"))),\"_\")).dropDuplicates([\"entity_pair\"]).drop(\"entity_pair\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.window import Window as W\n",
    "v = df_ner_all.groupBy(\"entity\").agg(collect_set(\"section_id\").alias(\"sections\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[entity: string, sections: array<string>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+---+\n",
      "|              entity|            sections| id|\n",
      "+--------------------+--------------------+---+\n",
      "|\"Taxifolin 3 ' - ...|           [1278739]|  1|\n",
      "|$ \\ beta $ - hairpin|           [1539578]|  2|\n",
      "|         ( E , E ) -|           [1503298]|  3|\n",
      "|- 19 and H1N1 swi...|  [5220609, 1411652]|  4|\n",
      "|              - Nrf2|           [6906166]|  5|\n",
      "|        - nCV N gene|           [3823453]|  6|\n",
      "|         - occlusion|   [1123441, 642104]|  7|\n",
      "|- related infections|   [1354288, 665292]|  8|\n",
      "|1 , 2 , 3 , 4 - t...|[1291599, 8620851...|  9|\n",
      "|1 , 3 , 6 - trihy...|           [1538143]| 10|\n",
      "|       1 microglobul|           [6290762]| 11|\n",
      "|                 13C|[874533, 1470080,...| 12|\n",
      "|     17β - estradiol|           [3906171]| 13|\n",
      "|     1ab polyprotein|[622787, 920614, ...| 14|\n",
      "|2 , 2 ' - Azino -...|            [853792]| 15|\n",
      "|2019 - nCoV main ...|[682594, 7808126,...| 16|\n",
      "|2019 Corona Virus...|[7274121, 1352238...| 17|\n",
      "|3 , 5 - Di - O - ...|  [1064612, 3040722]| 18|\n",
      "|3 - to - 5 exonuc...|           [5398839]| 19|\n",
      "|     3CL pro complex|  [5569142, 1583834]| 20|\n",
      "+--------------------+--------------------+---+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "v = v.withColumn(\"idx\", monotonically_increasing_id())\n",
    "windowSpec = W.orderBy(\"idx\")\n",
    "v = v.withColumn(\"id\", row_number().over(windowSpec)).drop(\"idx\")\n",
    "v.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------------------+-----+\n",
      "|    entity|            sections|   id|\n",
      "+----------+--------------------+-----+\n",
      "|COVID - 19|[6430795, 1018353...|45423|\n",
      "+----------+--------------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "v.filter(\"entity = 'COVID - 19'\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+-----------+\n",
      "|          entity_src|          entity_dst|re_type_src|\n",
      "+--------------------+--------------------+-----------+\n",
      "|sesquiterpen hydr...|( E ) - ß - farne...|      euadr|\n",
      "|                   ,|   Alloyohimbine , ,|      euadr|\n",
      "|                 3CL|           - ACE - 2|      euadr|\n",
      "|  palindrome repeats|               - Cas|      euadr|\n",
      "|             - LARP1|        5 ′ TOP mRNA|        GAD|\n",
      "|                coki|            / IL - 6|        GAD|\n",
      "|1 , 10 - phenanth...|[ Co ( indo - O ,...|      euadr|\n",
      "|                 111|               human|      euadr|\n",
      "|14 - 3 - 3 protei...|               YWHAE|      euadr|\n",
      "|       spike protein|2 - O - ribose me...|        GAD|\n",
      "|                trip|             2 ′ - C|      euadr|\n",
      "|         2019 - nCoV|      angiotensin II|      euadr|\n",
      "|receptor binding ...|        2019 - spike|      euadr|\n",
      "|      SARS - CoV - 2|            2019 nCO|      euadr|\n",
      "|             24 , 25|                   e|      euadr|\n",
      "|             Aularin|3 , 5 - Di - O - ...|      euadr|\n",
      "|                ACE2|          3CLpro ‐ 1|      euadr|\n",
      "|      5p - replicase|           orf1 / ab|      euadr|\n",
      "|7 Pangolin - Cov ...|                ORFs|      euadr|\n",
      "|        stem - loops|       A - site tRNA|      euadr|\n",
      "+--------------------+--------------------+-----------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "e_drop_duplicate = e_drop_duplicate.select(\"entity_src\",\"entity_dst\",\"re_type_src\")\n",
    "# e = e.withColumnRenamed(\"entity_src\", \"src\").withColumnRenamed(\"entity_dst\", \"dst\") \\\n",
    "# .withColumnRenamed(\"re_type_src\", \"relationship\")\n",
    "e_drop_duplicate.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_drop_duplicate = e_drop_duplicate.join(v, e_drop_duplicate.entity_src == v.entity).select(\"id\",\"entity_dst\",\"re_type_src\").withColumnRenamed(\"id\",\"src\")\n",
    "e = e_drop_duplicate.join(v, e_drop_duplicate.entity_dst == v.entity).select(\"id\",\"src\",\"re_type_src\").withColumnRenamed(\"id\",\"dst\").withColumnRenamed(\"re_type_src\",\"relationship\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[dst: int, src: int, relationship: string]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+-----+------------+\n",
      "|dst|  src|relationship|\n",
      "+---+-----+------------+\n",
      "|  1|26113|       euadr|\n",
      "|  1|50272|       euadr|\n",
      "|  1|77094|       euadr|\n",
      "|  1|34433|       euadr|\n",
      "|  5|53014|       euadr|\n",
      "|  9|18661|         GAD|\n",
      "| 10|50656|       euadr|\n",
      "| 13|28406|       euadr|\n",
      "| 13|95216|       euadr|\n",
      "| 18|48101|       euadr|\n",
      "| 18| 4370|       euadr|\n",
      "| 18|26521|       euadr|\n",
      "| 18|93377|       euadr|\n",
      "| 19|17642|       euadr|\n",
      "| 19|33829|         GAD|\n",
      "| 19|91314|         GAD|\n",
      "| 19|47794|         GAD|\n",
      "| 19|70152|         GAD|\n",
      "| 19|93139|         GAD|\n",
      "| 19|32219|       euadr|\n",
      "+---+-----+------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "e.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphframes import *\n",
    "g = GraphFrame(v,e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "gad_g = g.filterVertices(\"entity like '%COVID%'\").dropIsolatedVertices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "gad_g = g.filterEdges(\"src = 45423 or dst = 45423\").dropIsolatedVertices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "283"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gad_g.vertices.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gad_g.edges.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "gad_g_v = gad_g.vertices.select(\"id\").rdd.flatMap(lambda x: x).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = [{'name': i[\"entity\"], 'size': i[\"size\"]}\n",
    "         for i in gad_g.vertices.select(\"entity\", size(\"sections\").alias(\"size\")).collect()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "links = [{'source': gad_g_v.index(u[\"source\"]), 'target': gad_g_v.index(u[\"target\"])}\n",
    "         for u in gad_g.edges.withColumnRenamed(\"dst\",\"target\").withColumnRenamed(\"src\",\"source\").select(\"source\",\"target\",\"relationship\").collect()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = {'nodes': nodes, \"links\": links}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nodes': [{'name': 'COVID - 19 distress', 'size': 8},\n",
       "  {'name': 'COVID - op', 'size': 1},\n",
       "  {'name': 'severe COVID - 19', 'size': 12},\n",
       "  {'name': 'COVID - 19D - 19', 'size': 37},\n",
       "  {'name': 'COVID - 19VI - 19', 'size': 6},\n",
       "  {'name': 'COVID - 19 A', 'size': 3},\n",
       "  {'name': 'SARS - CoV - 2 and COVID - 19', 'size': 3},\n",
       "  {'name': 'COVID - 19 infections', 'size': 584},\n",
       "  {'name': 'Coronavirus COVID - 19', 'size': 10},\n",
       "  {'name': 'severe and critical COVID - 19', 'size': 2},\n",
       "  {'name': 'COVID - 19 anti', 'size': 5},\n",
       "  {'name': 'COVID ) of', 'size': 2},\n",
       "  {'name': 'COVID - 19 pneumonia', 'size': 2415},\n",
       "  {'name': 'COVID - 19 health', 'size': 12},\n",
       "  {'name': 'COVID of', 'size': 4},\n",
       "  {'name': 'COVID related pneumonia', 'size': 2},\n",
       "  {'name': 'COVID - 19 associated pulmonary aspergillosis', 'size': 2},\n",
       "  {'name': 'COVID - 19 hypoxemia', 'size': 1},\n",
       "  {'name': 'COVID ‐ 9', 'size': 1},\n",
       "  {'name': 'COVID19 virus', 'size': 5},\n",
       "  {'name': 'COD - 19 CT CX COVID - 19 CXR', 'size': 2},\n",
       "  {'name': 'novel coronavirus ( COVID - 19 ) disease', 'size': 4},\n",
       "  {'name': 'COVID - 19 virus', 'size': 508},\n",
       "  {'name': 'COVID19 disease', 'size': 12},\n",
       "  {'name': 'COVID - 19 infectious diseases', 'size': 2},\n",
       "  {'name': 'COVID - 19 syndrome Coronavirus 2', 'size': 1},\n",
       "  {'name': 'COVID - 19 - SE', 'size': 2},\n",
       "  {'name': 'COVID ’ s - 19', 'size': 1},\n",
       "  {'name': 'coronavirus disease 2019 ( COVID - 19 )', 'size': 9},\n",
       "  {'name': 'COVID - 19 vasculopathy', 'size': 2},\n",
       "  {'name': 'COVID COVID -', 'size': 1},\n",
       "  {'name': 'COVID infection', 'size': 62},\n",
       "  {'name': 'COVID ‐ 19 viral infection', 'size': 2},\n",
       "  {'name': 'COVID ‐ 19 disease', 'size': 90},\n",
       "  {'name': 'COVID - 19 anosmia', 'size': 8},\n",
       "  {'name': 'COVIDVI', 'size': 19},\n",
       "  {'name': 'COVID ‐ 19 related', 'size': 5},\n",
       "  {'name': 'COVID - 19 cough', 'size': 8},\n",
       "  {'name': 'COVID ‐ 19 chilblains', 'size': 2},\n",
       "  {'name': 'COVID ‐ 19 and', 'size': 1},\n",
       "  {'name': 'non - COVID viral pneumonia', 'size': 6},\n",
       "  {'name': 'COVID - 19 im', 'size': 2},\n",
       "  {'name': 'COVID - 19 lung infection', 'size': 5},\n",
       "  {'name': 'COVID - 19 coagulopathy', 'size': 20},\n",
       "  {'name': 'COVID 19 disease', 'size': 6},\n",
       "  {'name': 'COVIDheumatic', 'size': 2},\n",
       "  {'name': 'non - COVID - related diseases', 'size': 1},\n",
       "  {'name': 'COVID -ph', 'size': 1},\n",
       "  {'name': 'Coronavirus disease COVID - 19', 'size': 6},\n",
       "  {'name': 'COVID - 19 - related', 'size': 92},\n",
       "  {'name': 'COVID 19 virus', 'size': 13},\n",
       "  {'name': 'COVID -D', 'size': 8},\n",
       "  {'name': 'COVID - infected', 'size': 2},\n",
       "  {'name': 'COVID ‐ 19 pneumonia', 'size': 93},\n",
       "  {'name': 'COVID ‐ related', 'size': 1},\n",
       "  {'name': 'COVID - 19 lung infections', 'size': 7},\n",
       "  {'name': 'COVID - 19VID - 19', 'size': 37},\n",
       "  {'name': 'COVID - infections', 'size': 2},\n",
       "  {'name': 'COVID - 19 viruses', 'size': 9},\n",
       "  {'name': 'COVID - 2019', 'size': 230},\n",
       "  {'name': 'COVID ‐ 19 ‐ related kidney disease', 'size': 1},\n",
       "  {'name': 'COVID - 19 )', 'size': 65},\n",
       "  {'name': 'COVID - 19 Grave - Coronavirus 2', 'size': 1},\n",
       "  {'name': 'COVID - 19 genome sequences', 'size': 1},\n",
       "  {'name': 'COVID - 19 19', 'size': 35},\n",
       "  {'name': 'COVID - 19 d', 'size': 18},\n",
       "  {'name': 'COVID - 19 CXR', 'size': 2},\n",
       "  {'name': 'COVID - 19 infectionuria', 'size': 1},\n",
       "  {'name': 'COVID - 19 and clavicle', 'size': 1},\n",
       "  {'name': 'COVID - 19 disease', 'size': 1737},\n",
       "  {'name': 'COVID - 19 pneumonitis', 'size': 34},\n",
       "  {'name': 'vere COVID - 19', 'size': 3},\n",
       "  {'name': 'COVID - 19 co', 'size': 43},\n",
       "  {'name': 'COVID 19D', 'size': 1},\n",
       "  {'name': 'COVID19 infection', 'size': 51},\n",
       "  {'name': 'neurological disorders COVID - 19', 'size': 2},\n",
       "  {'name': 'COVID - 19 associated pneumonia', 'size': 38},\n",
       "  {'name': 'COVID disease', 'size': 51},\n",
       "  {'name': 'COVID - 19 irritation', 'size': 2},\n",
       "  {'name': 'non - COVID related illness', 'size': 3},\n",
       "  {'name': 'COVID - 19 ) Coronavirus disease', 'size': 1},\n",
       "  {'name': 'COVID - 19 chilblains', 'size': 7},\n",
       "  {'name': 'COVID -VID', 'size': 12},\n",
       "  {'name': 'COVID ‐ 19 related co', 'size': 1},\n",
       "  {'name': 'human COVID - 19', 'size': 39},\n",
       "  {'name': 'COVID - 19 pulmonary infection', 'size': 4},\n",
       "  {'name': 'COVID - 19VI', 'size': 30},\n",
       "  {'name': 'COVID - 19 - n', 'size': 1},\n",
       "  {'name': 'COVID - 19 neurologic symptoms', 'size': 1},\n",
       "  {'name': 'COVID pneumonia', 'size': 27},\n",
       "  {'name': 'COVID - 19 , coronavirus disease', 'size': 2},\n",
       "  {'name': 'COVID - 19virus', 'size': 51},\n",
       "  {'name': 'COVID - 19 -', 'size': 100},\n",
       "  {'name': 'COVIDD', 'size': 11},\n",
       "  {'name': 'COVID ‐ 19 infection', 'size': 213},\n",
       "  {'name': 'COVID - 19 viral', 'size': 35},\n",
       "  {'name': 'COVID ‐ 19 infections', 'size': 17},\n",
       "  {'name': 'COVID - 19ious and Tropical', 'size': 1},\n",
       "  {'name': 'SARS - CoV - 2 COVID - 19', 'size': 31},\n",
       "  {'name': 'COVID - 19 damage', 'size': 5},\n",
       "  {'name': 'pande COVID', 'size': 1},\n",
       "  {'name': 'COVID - 19 pneumonia en', 'size': 4},\n",
       "  {'name': 'COVID - 19ronavirus', 'size': 3},\n",
       "  {'name': 'COVID - 19 - related kidney disease', 'size': 1},\n",
       "  {'name': 'COVID - 19 hemodialys hemodialys', 'size': 1},\n",
       "  {'name': 'COVID - 9', 'size': 14},\n",
       "  {'name': 'non - COVID - 19 severe respiratory viral infections', 'size': 2},\n",
       "  {'name': 'COVID - 19 gene sequences', 'size': 1},\n",
       "  {'name': 'COVID - 19T', 'size': 4},\n",
       "  {'name': 'Co COVID', 'size': 1},\n",
       "  {'name': 'COVID - 19 19 19', 'size': 2},\n",
       "  {'name': 'COVID - 19 hypoxia', 'size': 1},\n",
       "  {'name': 'COVID - 19 - associated', 'size': 47},\n",
       "  {'name': 'COVID - 19 respiratory syndrome', 'size': 4},\n",
       "  {'name': 'COVID - infection', 'size': 12},\n",
       "  {'name': 'COVID ‐ 19 viral', 'size': 3},\n",
       "  {'name': 'COVID - 19 syndrome', 'size': 43},\n",
       "  {'name': 'COVIDVID ground - glass opacity', 'size': 1},\n",
       "  {'name': 'COVID - UrolithiasD - 19', 'size': 1},\n",
       "  {'name': 'COVID - 19 - associated pulmonary aspergillosis', 'size': 6},\n",
       "  {'name': 'non - COVID - 19 respiratory disease', 'size': 1},\n",
       "  {'name': 'COVID - 19 illness', 'size': 186},\n",
       "  {'name': 'COVID - induced', 'size': 1},\n",
       "  {'name': 'COVID - 19VID -VID', 'size': 1},\n",
       "  {'name': 'COVID - 19 pneumonia COVID - 19 pneumonia', 'size': 11},\n",
       "  {'name': 'COVID - 19 ) virus', 'size': 18},\n",
       "  {'name': 'coronavirus ( COVID - 19', 'size': 20},\n",
       "  {'name': 'coronavirus ( COVID - 19 fulminant myopericarditis', 'size': 1},\n",
       "  {'name': 'respiratory disease COVID - 19', 'size': 1},\n",
       "  {'name': 'COVID - 19 antibody', 'size': 31},\n",
       "  {'name': 'COVID - 19', 'size': 135604},\n",
       "  {'name': 'Coronavirus disease ( COVID - 19', 'size': 3},\n",
       "  {'name': 'COVID - 19 paraplegic', 'size': 2},\n",
       "  {'name': 'COVID - 19 related pneumonia', 'size': 15},\n",
       "  {'name': 'COVID - 19 tracheost', 'size': 1},\n",
       "  {'name': 'COVID - 19 COVID - 19', 'size': 8},\n",
       "  {'name': 'coronavirus disease 2019 ( COVID - 19 ) infection', 'size': 36},\n",
       "  {'name': 'novel coronavirus ( COVID - 19 ) infected pneumonia', 'size': 3},\n",
       "  {'name': 'COVID - 19 cardiac complications', 'size': 1},\n",
       "  {'name': 'COVID - 19V - 19', 'size': 2},\n",
       "  {'name': 'COVID - 19 infectious disease', 'size': 14},\n",
       "  {'name': 'COVID - 19 pneumonia con opities', 'size': 2},\n",
       "  {'name': 'COVID - 19 pneumopathy', 'size': 4},\n",
       "  {'name': 'nCOVID - 19', 'size': 29},\n",
       "  {'name': 'COVID - 19 complications', 'size': 60},\n",
       "  {'name': 'COVID - 19int', 'size': 15},\n",
       "  {'name': 'COVID - 19 conditions', 'size': 10},\n",
       "  {'name': 'COVID - 19 infected', 'size': 55},\n",
       "  {'name': 'COVID - 9 infection', 'size': 2},\n",
       "  {'name': 'COVID necrotizing leukoencephalopathy', 'size': 2},\n",
       "  {'name': 'COVID - 19 related', 'size': 67},\n",
       "  {'name': 'COVID - 19a', 'size': 2},\n",
       "  {'name': 'COVID - related', 'size': 1},\n",
       "  {'name': '[UNK] COVID', 'size': 1},\n",
       "  {'name': 'COVID - 19 infected pneumonia', 'size': 4},\n",
       "  {'name': 'COVID heart', 'size': 3},\n",
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       "  {'source': 161, 'target': 282}]}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('graph.json', 'w') as f:\n",
    "    json.dump(graph, f, indent=4,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "gp = nx.from_pandas_edgelist(g.edges.where(col(\"dst\")==\"1\").toPandas(),'src','dst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6));\n",
    "nx.draw_networkx(gp, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.karate_club_graph()"
   ]
  }
 ],
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