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arxiv/arxiv.md

@@ -17,7 +17,7 @@ Node classification tasks in the arxiv dataset include predicting the paper's ca
 
 * Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia.Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1):396–413, 2020
 
-You can directly download the raw dataset without edge texts (citation context) from the following codes:
+You can directly get the raw dataset from following codes:
 
 ```python
 from ogb.nodeproppred import PygNodePropPredDataset

arxiv/emb/arxiv_bert_base_uncased_512_cls_edge.pt

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+size 1791350701

arxiv/emb/arxiv_bert_base_uncased_512_cls_node.pt

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+size 260112301

arxiv/emb/arxiv_bert_large_uncased_512_cls_edge.pt

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+size 2388467122

arxiv/emb/arxiv_bert_large_uncased_512_cls_node.pt

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+size 346815922

arxiv/processed/arxiv.pkl

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+oid sha256:2c79dbeaf0b9bc1458c04d385c492742987cd02a08beda123387ca392cb27e59
+size 389843902

arxiv/tmp.ipynb

@@ -0,0 +1,109 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lizhuofeng/.local/lib/python3.10/site-packages/torch/storage.py:414: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  return torch.load(io.BytesIO(b))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data(edge_index=[2, 1166243], text_nodes=[169343], text_edges=[1166243], node_labels=[169343])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle \n",
+    "\n",
+    "with open('processed/arxiv.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "count = 0\n",
+    "for j, i, in enumerate(data.text_nodes):\n",
+    "    if i == None:\n",
+    "        count += 1\n",
+    "        data.text_nodes[j] = ''\n",
+    "\n",
+    "\n",
+    "for j, i in enumerate(data.text_edges):\n",
+    "    if i == None:\n",
+    "        count += 1\n",
+    "        data.text_edges[j] = ''\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('processed/arxiv.pkl', 'wb') as f:\n",
+    "    pickle.dump(data, f)"
+   ]
+  },
+  {
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

twitter/emb/tweets_bert_base_uncased_512_cls_edge.pt

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 size 114688434

twitter/emb/tweets_bert_base_uncased_512_cls_node.pt

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 size 93367154

twitter/emb/tweets_bert_large_uncased_512_cls_edge.pt

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twitter/emb/tweets_bert_large_uncased_512_cls_node.pt

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+size 124489143

twitter/processed/twitter.pkl

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+size 7971933

twitter/raw/process_final_twitter.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,14 +31,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "67682it [00:04, 15665.18it/s]\n"
+      "67682it [00:04, 14848.15it/s]\n"
      ]
     }
    ],
@@ -50,21 +50,25 @@
     "text_edges = []\n",
     "text_nodes = [-1] * len(df) * 20\n",
     "count = 0\n",
+    "node_labels = [-1] * len(df) * 20\n",
     "\n",
     "# Use df instead of g for iteration\n",
     "for _, row in tqdm.tqdm(df.iterrows()):\n",
     "    # Convert tweet_id and user_id to string to ensure consistency\n",
-    "    tweet_id = str(row['tweet_id'])\n",
-    "    user_id = str(row['user_id'])\n",
-    "    \n",
+    "    tweet_id = str(row[\"tweet_id\"])\n",
+    "    user_id = str(row[\"user_id\"])\n",
+    "\n",
     "    if tweet_id not in tweet_id2idx:\n",
     "        tweet_id2idx[tweet_id] = count\n",
     "        tweet.append(count)\n",
     "        count += 1\n",
     "    else:\n",
     "        tweet.append(tweet_id2idx[tweet_id])\n",
-    "    text_nodes[tweet_id2idx[tweet_id]] = f\"tweet{tweet_id2idx[tweet_id]} of event{row['event_id']}\"\n",
-    "    \n",
+    "    text_nodes[tweet_id2idx[tweet_id]] = (\n",
+    "        f\"tweet\"\n",
+    "    )\n",
+    "    node_labels[tweet_id2idx[tweet_id]] = str(row[\"event_id\"])\n",
+    "\n",
     "    if user_id not in user_id2idx:\n",
     "        user_id2idx[user_id] = count\n",
     "        user.append(count)\n",
@@ -72,10 +76,11 @@
     "    else:\n",
     "        user.append(user_id2idx[user_id])\n",
     "    text_nodes[user_id2idx[user_id]] = f\"user\"\n",
-    "    \n",
-    "    text_edges.append(row['text'])\n",
-    "    \n",
-    "    for mention in row['user_mentions']:\n",
+    "    node_labels[user_id2idx[user_id]] = -1\n",
+    "\n",
+    "    text_edges.append(row[\"text\"])\n",
+    "\n",
+    "    for mention in row[\"user_mentions\"]:\n",
     "        if mention not in user_id2idx:\n",
     "            user_id2idx[mention] = count\n",
     "            user.append(count)\n",
@@ -84,21 +89,23 @@
     "            user.append(user_id2idx[mention])\n",
     "        tweet.append(tweet_id2idx[tweet_id])\n",
     "        text_nodes[user_id2idx[mention]] = f\"mentioned user\"\n",
-    "        text_edges.append(row['text'])"
+    "        node_labels[user_id2idx[mention]] = -1\n",
+    "        text_edges.append(row[\"text\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "text_nodes = text_nodes[:count]"
+    "text_nodes = text_nodes[:count]\n",
+    "node_labels = node_labels[:count]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,26 +113,20 @@
     "graph = Data(\n",
     "\t\t\ttext_nodes=text_nodes,\n",
     "\t\t\ttext_edges=text_edges,\n",
-    "\t\t\tedge_index=torch.tensor(edge_index, dtype=torch.long)\n",
+    "\t\t\tedge_index=torch.tensor(edge_index, dtype=torch.long),\n",
+    "\t\t\tnode_labels=node_labels\n",
     "\t\t)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "with open('../processed/twitter.pkl', 'wb') as f:\n",
     "    pickle.dump(graph, f)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {