This data set was introduced by Dou et al. in Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters.
The data and paper's code could be found here, and here I cheated during the processing of data by leveraging dgl to convert ajacency matrix to edgelist, and nodes with features & label.
Schema of the data:
- vertices: Yelp Reviews, with label(is_fruad) as a property and 32 normalized features as properties.
- edges: Relationship between reviews without property.
- R-U-R: shares_user_with
- R-S-R: shares_restaurant_rating_with
- R-T-R: shares_restaurant_in_one_month_with
python3 -m pip install -r requirements.txt
python3 data_download.py
ls -l data/*.csvGenerated files:
$ ls data/*.csv
$
net_rsr.csv net_rtr.csv net_rur.csv vertices.csvAssuming that we boostrap a NebulaGraph with Nebula-UP.
docker run --rm -ti \
--network=nebula-net \
-v ${PWD}/yelp_nebulagraph_importer.yaml:/root/importer.yaml \
-v ${PWD}/data:/root \
vesoft/nebula-importer:v3.1.0 \
--config /root/importer.yamlAfter it's imported, we could query the stats of the graph:
~/.nebula-up/console.sh -e "USE yelp; SHOW STATS"It should be like this:
(root@nebula) [(none)]> USE yelp; SHOW STATS
+---------+---------------------------------------+---------+
| Type | Name | Count |
+---------+---------------------------------------+---------+
| "Tag" | "review" | 45954 |
| "Edge" | "shares_restaurant_in_one_month_with" | 1147232 |
| "Edge" | "shares_restaurant_rating_with" | 6805486 |
| "Edge" | "shares_user_with" | 98630 |
| "Space" | "vertices" | 45954 |
| "Space" | "edges" | 8051348 |
+---------+---------------------------------------+---------+
Got 6 rows (time spent 1911/4488 us)I know I don't have to do this as we have it in DGL dataset already, this is just a demo of how to use NebulaGraph with DGL.
In [1]:
from nebula_dgl import NebulaLoader
nebula_config = {
"graph_hosts": [
('graphd', 9669),
('graphd1', 9669),
('graphd2', 9669)
],
"user": "root",
"password": "nebula",
}
with open('nebulagraph_yelp_dgl_mapper.yaml', 'r') as f:
feature_mapper = yaml.safe_load(f)
nebula_loader = NebulaLoader(nebula_config, feature_mapper)
g = nebula_loader.load()
# This will take a while
In [2]: g
Out[2]:
Graph(num_nodes={'review': 45954},
num_edges={('review', 'shares_restaurant_in_one_month_with', 'review'): 1147232, ('review', 'shares_restaurant_rating_with', 'review'): 6805486, ('review', 'shares_user_with', 'review'): 98630},
metagraph=[('review', 'review', 'shares_restaurant_in_one_month_with'), ('review', 'review', 'shares_restaurant_rating_with'), ('review', 'review', 'shares_user_with')])
In [3]: g.canonical_etypes
Out[3]:
[('review', 'shares_restaurant_in_one_month_with', 'review'),
('review', 'shares_restaurant_rating_with', 'review'),
('review', 'shares_user_with', 'review')]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent