# congrat

**Repository Path**: mmmz2/congrat

## Basic Information

- **Project Name**: congrat
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-26
- **Last Updated**: 2024-07-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Joint embeddings of text and graph structures
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We want to learn a single model of the joint distribution of text and a graph
structure, where the graph is over the entities generating the text. (This
latter condition is what distinguishes our case from models using knowledge
graphs.) This problem occurs in a variety of settings: the follow graph among
users who post tweets, link graphs between web pages, citation networks for
academic articles, etc. We view it as a kind of multimodal learning, allowing
models to leverage graph data that co-occurs with texts.