eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks

Overview

This repository is PyTorch implementation of Competitive Graph Neural Network (CGNN) proposed in

"eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks".

1. Requirements

• numpy == 1.19.5

• torch == 1.6.0

• scikit-learn == 0.23.2

• scipy == 1.4.1

2. MOOC student drop-out

2.1 Data

In the dataset MOOC student drop-out, we regard students as users and actions as items;

• action_features.mat : the attributes of edges, the last column is the labels of edges;

• bipartitie_action.mat : the bipartitie graph of students and actions;

• item_features_matrix.mat : the attrbutes of actions;

• user_features_matrix.mat : the attrbutes of students;

• edge_item_features_matrix.mat : the concatenation of attributes of edges and corresponding actions;

• edge_item_features_matrix.mat : the concatenation of attributes of edges and corresponding students.

2.2 Structure

• MOOC student dropout/data/new_mooc.mat: the dataset which contains action_features.mat bipartitie_action.mat item_features_matrix.mat user_features_matrix.mat edge_item_features_matrix.mat edge_item_features_matrix.mat

• MOOC student dropout/main.py: training the model and training options;

• MOOC student dropout/model.py: CGNN implementaions;

• MOOC student dropout/preprocess.py: utils;

• MOOC student dropout/dgi.py: CGNN implementaions;

2.3 Run

To train the model, run MOOC student dropout/main.py

3. Bitcoin-Alpha

3.1 Data

Bitcoin-Alpha/data/alpha/alpha_graph_u2u.pickle: the pickled sparse adjacency matrix about users;

Bitcoin-Alpha/data/alpha/alpha_graph_u2p.pickle: the pickled sparse adjacency matrix about users and items;

Bitcoin-Alpha/data/alpha/alpha_labels.pickle: the pickled user labels.

3.2 Structure

• Bitcoin-Alpha/aggregators.py: the convolution layers in GraphSAGE implementations;

• Bitcoin-Alpha/encoders.py: CGNN implementations;

• Bitcoin-Alpha/model.py: CGNN implementations and training the model.

3.3 Run

To train the model, run Bitcion-Alpha/model.py