论文标题
GlobalWalk：通过偏置抽样学习全球感知的节点嵌入
GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling
论文作者
论文摘要
流行的节点嵌入方法（例如DeepSwalk）遵循在图表上进行随机步行的范式，然后要求每个节点与与之一起出现的节点均接近。尽管事实证明在各种任务中都是成功的，但此范式将带有拓扑的图形缩小为一组顺序句子，从而省略了全局信息。为了产生全球感知的节点嵌入，我们提出了Global-Walk，这是一种有偏见的随机步行策略，有利于具有相似语义的节点。经验证据表明，全球步道通常可以提高全球对产生的嵌入的认识。
Popular node embedding methods such as DeepWalk follow the paradigm of performing random walks on the graph, and then requiring each node to be proximate to those appearing along with it. Though proved to be successful in various tasks, this paradigm reduces a graph with topology to a set of sequential sentences, thus omitting global information. To produce global-aware node embeddings, we propose GlobalWalk, a biased random walk strategy that favors nodes with similar semantics. Empirical evidence suggests GlobalWalk can generally enhance global awareness of the generated embeddings.
