Edge2Node: Reducing Edge Prediction to Node Classification

• URL: http://arxiv.org/abs/2311.02921v3
• Date: Wed, 22 Nov 2023 17:26:08 GMT
• Title: Edge2Node: Reducing Edge Prediction to Node Classification
• Authors: Zahed Rahmati
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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