DeepWalk是最早提出的基于 Word2vec 的节点向量化模型。其主要思路,就是利用构造节点在网络上的随机游走路径,来模仿文本生成的过程,提供一个节点序列,然后用Skip-gram和Hierarchical Softmax模型对随机游走序列中每个局部窗口内的节点对进行概率建模,最大化随机游走序列的似然概率,并使用最终随机梯度下降学习参数。

最新论文

This article aims to study the topological invariant properties encoded in node graph representational embeddings by utilizing tools available in persistent homology. Specifically, given a node embedding representation algorithm, we consider the case when these embeddings are real-valued. By viewing these embeddings as scalar functions on a domain of interest, we can utilize the tools available in persistent homology to study the topological information encoded in these representations. Our construction effectively defines a unique persistence-based graph descriptor, on both the graph and node levels, for every node representation algorithm. To demonstrate the effectiveness of the proposed method, we study the topological descriptors induced by DeepWalk, Node2Vec and Diff2Vec.

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