Research on Graph Embedding Optimization Method Based on the Fusion of Node2vec and GCN

本文结合了Node2vec和GCN这两种方法，先利用Node2vec方法得到初步的图嵌入，之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务，比较了结合前后方法的表现，验证了其有效性。In this paper, we integrate the Node2vec and GCN methods. Initially, the Node2vec method is employed to obtain preliminary graph embeddings, which are then used as input to further update the graph embedding matrix through GCN. The study selects the Wikipedia dataset for node classification tasks, comparing the performance of the methods before and after integration to validate their effectiveness.