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goodreads_children/emb/children_bert_base_uncased_512_cls_edge.pt

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goodreads_children/emb/children_bert_base_uncased_512_cls_node.pt

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goodreads_children/goodreads.md

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+# Goodreads Datasets
+
+## Dataset Description
+The Goodreads datasets consist of four datasets, specifically labeled as Goodreads-History, Goodreads-Crime, Goodreads-Children, and Goodreads-Cosmics. The Goodreads datasets are a user-book review network. It includes information about books, users and reviews. Nodes represent books and users. Text on a book node is the description of the book. Text on a user node is the `user`.  The book text includes the following information: `The book [title] is a [format] edition published by [publisher] in [publication_month] [publication_year] about [description], consisting of [num_pages].` Edges represent relationships between books and users. Text on an edge means a user's review of a book. 
+
+
+## Graph Machine Learning Tasks
+
+### Link Prediction
+Link prediction in the Goodreads dataset involves predicting potential connections between users and books.  The goal is to predict whether a user will review a book.
+
+### Node Classification
+Node classification tasks in the Goodreads dataset include predicting the book's category. 

goodreads_comics/emb/comics_bert_base_uncased_512_cls_edge.pt

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goodreads_comics/emb/comics_bert_base_uncased_512_cls_node.pt

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goodreads_comics/emb/comics_openai-old_node.pt

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goodreads_comics/emb/comics_openai_edge.pt

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goodreads_comics/goodreads.md

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+# Goodreads Datasets
+
+## Dataset Description
+The Goodreads datasets consist of four datasets, specifically labeled as Goodreads-History, Goodreads-Crime, Goodreads-Children, and Goodreads-Cosmics. The Goodreads datasets are a user-book review network. It includes information about books, users and reviews. Nodes represent books and users. Text on a book node is the description of the book. Text on a user node is the `user`.  The book text includes the following information: `The book [title] is a [format] edition published by [publisher] in [publication_month] [publication_year] about [description], consisting of [num_pages].` Edges represent relationships between books and users. Text on an edge means a user's review of a book. 
+
+
+## Graph Machine Learning Tasks
+
+### Link Prediction
+Link prediction in the Goodreads dataset involves predicting potential connections between users and books.  The goal is to predict whether a user will review a book.
+
+### Node Classification
+Node classification tasks in the Goodreads dataset include predicting the book's category. 

goodreads_crime/emb/crime_bert_base_uncased_512_cls_edge.pt

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goodreads_crime/emb/crime_bert_base_uncased_512_cls_node.pt

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goodreads_crime/goodreads.md

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+# Goodreads Datasets
+
+## Dataset Description
+The Goodreads datasets consist of four datasets, specifically labeled as Goodreads-History, Goodreads-Crime, Goodreads-Children, and Goodreads-Cosmics. The Goodreads datasets are a user-book review network. It includes information about books, users and reviews. Nodes represent books and users. Text on a book node is the description of the book. Text on a user node is the `user`.  The book text includes the following information: `The book [title] is a [format] edition published by [publisher] in [publication_month] [publication_year] about [description], consisting of [num_pages].` Edges represent relationships between books and users. Text on an edge means a user's review of a book. 
+
+
+## Graph Machine Learning Tasks
+
+### Link Prediction
+Link prediction in the Goodreads dataset involves predicting potential connections between users and books.  The goal is to predict whether a user will review a book.
+
+### Node Classification
+Node classification tasks in the Goodreads dataset include predicting the book's category. 

goodreads_history/goodreads.md

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+# Goodreads Datasets
+
+## Dataset Description
+The Goodreads datasets consist of four datasets, specifically labeled as Goodreads-History, Goodreads-Crime, Goodreads-Children, and Goodreads-Cosmics. The Goodreads datasets are a user-book review network. It includes information about books, users and reviews. Nodes represent books and users. Text on a book node is the description of the book. Text on a user node is the `user`.  The book text includes the following information: `The book [title] is a [format] edition published by [publisher] in [publication_month] [publication_year] about [description], consisting of [num_pages].` Edges represent relationships between books and users. Text on an edge means a user's review of a book. 
+
+
+## Graph Machine Learning Tasks
+
+### Link Prediction
+Link prediction in the Goodreads dataset involves predicting potential connections between users and books.  The goal is to predict whether a user will review a book.
+
+### Node Classification
+Node classification tasks in the Goodreads dataset include predicting the book's category. 

readme.md

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+# TEG Datasets
+
+## Dataset Format
+
+Each dataset is a [PyG Data object](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Dataset.html#torch_geometric.data.Dataset)  and is stored in the `processed` subdir following a unified format, with each attribute defined as follows:
+
+- `edge_index`: Graph connectivity in COO format with shape [2, num_edges] and type `torch.long`.
+- `text_nodes`: `List` contains textual information for each node in the graph.
+- `text_edges`: `List` contains textual information for each edge in the graph.
+- `node_labels`: labels or classes for each node in the graph.
+- `edge_labels`: labels or classes for each edge in the graph and type `torch.long`.
+
+
+## Embedding Data Format
+The embedding data is thrived from `text_nodes` and `text_edges` through PLM including:
+- [GPT](https://platform.openai.com/docs/guides/embeddings)
+- [BERT-base](https://huggingface.co/bert-base-uncased)
+- [BERT-large](https://huggingface.co/bert-large-uncased)
+
+**We will provide more TEG datasets and PLM embedding in the futrue!**
+
+
+
+
+
+
+

twitter/emb/tweets_bert_base_uncased_512_cls_edge.pt

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twitter/emb/tweets_bert_base_uncased_512_cls_node.pt

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twitter/processed/twitter.pkl

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twitter/twitter.md

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+# Twitter Datasets
+
+## Dataset Description
+The twitter dataset is a social network. Nodes represent tweets and users. Text on nodes is the description of the tweets or the users. Edge between a and tweet means that the user posts the tweet. Text on edges is contents of the tweets.
+
+
+## Graph Machine Learning Tasks
+
+### Link Prediction
+Link prediction in the tweet dataset involves predicting potential connections between tweets and users.  The goal is to predict whether a user will post a tweet.