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https://github.com/santiagoenriquega/artist_similarity_network
Semantic artist similarity to visualize artist networks based on Last.fm data. It utilizes Python with NetworkX for network analysis as well as Plotly and D3.js for interactive visualization.
https://github.com/santiagoenriquega/artist_similarity_network
d3 d3js network networkx networkx-graph plotly pyhton python3
Last synced: about 16 hours ago
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Semantic artist similarity to visualize artist networks based on Last.fm data. It utilizes Python with NetworkX for network analysis as well as Plotly and D3.js for interactive visualization.
- Host: GitHub
- URL: https://github.com/santiagoenriquega/artist_similarity_network
- Owner: SantiagoEnriqueGA
- Created: 2024-06-07T01:41:04.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-06-09T00:15:20.000Z (8 months ago)
- Last Synced: 2024-12-13T10:49:05.188Z (about 2 months ago)
- Topics: d3, d3js, network, networkx, networkx-graph, plotly, pyhton, python3
- Language: Python
- Homepage:
- Size: 7.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Semantic Artist Similarity Analysis
## Overview
This project analyzes artist similarity using data from the **Semantic Artist Similarity (SAS) Dataset**. The main components of the project include:
1. `SAS_networkx.py`: Python script for building and analyzing the artist similarity network using NetworkX.
2. `SAS_D3.html`: HTML file for visualizing the network using D3.js.
3. `network_data` folder: Contains saved edges, nodes, and the graph data.
4. `plots` folder: Contains saved plots generated from the code.
5. `dataset-artist-similarity` folder: Contains data from the SAS dataset.
## SAS Dataset
The data used in this project is obtained from the **SAS: Semantic Artist Similarity Dataset**, which consists of two datasets of artist entities with their corresponding biography texts and a list of the top-10 most similar artists within the datasets used as ground truth.
### Files in the Dataset
1. `mirex_gold_top10.txt` and `lastfmapi_gold_top10.txt`: These files contain the top-10 lists of artists for every artist in both datasets, identified by MusicBrainz ID.
2. `mb2uri_mirex.txt` and `mb2uri_lastfmapi.txt`: These files contain the MusicBrainz ID, Last.fm name, and DBpedia URI for each artist.
3. Biography Texts: The biography texts of each artist are stored as `.txt` files named with the MusicBrainz ID.
## SAS_networkx.py
This Python script (`SAS_networkx.py`) is used for building and analyzing the artist similarity network. It includes the following functionalities:
- **Graph Class**: Represents the artist similarity network and provides methods for adding nodes, edges, calculating metrics, and visualizing the network.
- **Parsing Functions**: Functions to parse data from the SAS dataset and add nodes/edges to the graph.
- **Visualization**: Functions to draw the network using NetworkX and Plotly.
### Usage
1. Instantiate the `Graph` class.
2. Parse artist and similarity data using `parse_artist_txt` and `parse_sim_artist_txt` methods.
3. Build the network and visualize it using various methods provided in the script.
4. Explore network metrics such as node degrees, furthest connectivity, and shortest paths.
### Example Usage
```python
from SAS_networkx import Graph
# Instantiate Graph
graph = Graph()
# Parse artist and similarity data
graph.parse_artist_txt("dataset-artist-similarity/LastFM/mb2uri_lastfmapi.txt")
graph.parse_sim_artist_txt("dataset-artist-similarity/LastFM/lastfmapi_gold.txt", num_similar=5)
# Build and visualize the network
graph.add_networkx_depth('switchfoot', loops=6)
graph.draw_networkx_node(True)
# Find furthest connectivity and shortest paths
furthest_nodes, max_connectivity = graph.find_nodes_with_furthest_connectivity()
shortest_path = graph.shortest_path('adnan sami', 'e-40')
# Visualize shortest path and full network using NetworkX and Plotly
graph.visualize_shortest_path('adnan sami', 'e-40')
graph.draw_plotly_network(path=shortest_path)
```
For detailed usage and additional functionalities, refer to the comments and docstrings within `SAS_networkx.py`.
### Example Plots
## SAS_D3.html
This HTML file (`SAS_D3.html`) provides a visualization of the artist similarity network using D3.js. It loads data asynchronously, creates nodes and edges, and allows for interactive exploration of the network.
### Usage
1. Open `SAS_D3.html` in a web browser.
2. Use the search box to find nodes by name.
3. Click on nodes to highlight them.