Model Structure

A SupervisedEdgeWise model consists of graph convolutional layers followed by several prediction layers.

First, the source and destination vertices of the target edge are processed through the convolutional layers. The forward pass through a convolutional layer for a vertex proceeds as follows:

1. A set of neighbors of the vertex is sampled.
2. The previous layer representations of the neighbors are mean-aggregated, and the aggregated features are concatenated with the previous layer representation of the vertex.
3. This concatenated vector is multiplied with weights, and a bias vector is added.
4. The result is normalized such that the layer output has unit norm.

The edge embedding layer concatenates the source vertex embedding, the edge features and the destination vertex embedding, and then forwards it through a linear layer to get the edge embedding.

The prediction layers are standard neural network layers.

The following describes a few use cases where SupervisedEdgeWise algorithm can be applied:

• Friends Recommendation: To predict future friendships or connections between users based on current social graph data.
• Customer Retention: To predict which customers are likely to stop using a service by analyzing interaction patterns within the customer support network or usage logs.
• Predictive Maintenance in IoT Networks: To forecast potential failures in IoT (Internet of Things) networks by classifying the edges (connections) between sensors and devices based on historical data. This helps in proactive maintenance scheduling.