Summary of Using Graph Neural Networks to Predict Local Culture, by Thiago H Silva and Daniel Silver
Using Graph Neural Networks to Predict Local Culture
by Thiago H Silva, Daniel Silver
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a graph neural network (GNN) approach to quantify the relational dynamics within neighbourhoods using multiple sources of information, including internal characteristics, past events, and group flows. By applying this methodology to a large-scale public dataset from Yelp, researchers demonstrate its potential for predicting local culture attributes. The findings suggest that either area demographics or group profiles (based on Yelp reviewer tastes) yield similar results in predicting local culture, with the latter being a promising alternative when local information is scarce. This approach could empower researchers and policymakers to leverage diverse data sources, even when local area information is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses computers to understand how neighbourhoods work together. It combines different types of information about neighbourhoods to predict what makes them unique. The researchers tested this method using data from Yelp, a popular website for finding and reviewing restaurants and shops. They found that either knowing the area’s demographics or understanding the preferences of people who live there can help predict what kind of culture a neighbourhood has. This approach could be useful for researchers and policymakers who want to understand how neighbourhoods work together. |
Keywords
* Artificial intelligence * Gnn * Graph neural network
