Edge2Node: Reducing Edge Prediction to Node Classification
This is an incremental idea for researchers in graph machine learning, aiming to simplify edge prediction tasks.
The paper tackles the challenge of edge prediction in graph neural networks by proposing Edge2Node, a method that reduces edge prediction to node classification by creating a new graph where edges become nodes, eliminating the need for a scoring function.
Despite the success of graph neural network models in node classification, edge prediction (the task of predicting missing or potential links between nodes in a graph) remains a challenging problem for these models. A common approach for edge prediction is to first obtain the embeddings of two nodes, and then a predefined scoring function is used to predict the existence of an edge between the two nodes. Here, we introduce a preliminary idea called Edge2Node which suggests to directly obtain an embedding for each edge, without the need for a scoring function. This idea wants to create a new graph H based on the graph G given for the edge prediction task, and then suggests reducing the edge prediction task on G to a node classification task on H. We anticipate that this introductory method could stimulate further investigations for edge prediction task.