论文标题
图形网络的光谱嵌入
Spectral Embedding of Graph Networks
论文作者
论文摘要
我们介绍了一个无监督的图形嵌入，该图形嵌入了本地节点的相似性和连接性以及全球结构。嵌入是基于广义图拉普拉斯式的，其特征向量将在单个表示中紧凑地捕获网络结构和邻居接近度。关键思想是将给定的图转换为其权重，通过通过该边缘的最短路径数量的比例来测量边缘的中心性，并在表示中使用其光谱专有权。测试所得的图网络表示，在包括社交网络和材料科学在内的数据分析任务中，对艺术的现象显示出显着改善。我们还测试了从人类SARS COV-2蛋白 - 蛋白质相互作用组中的淋巴分类方法。
We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian, whose eigenvectors compactly capture both network structure and neighborhood proximity in a single representation. The key idea is to transform the given graph into one whose weights measure the centrality of an edge by the fraction of the number of shortest paths that pass through that edge, and employ its spectral proprieties in the representation. Testing the resulting graph network representation shows significant improvement over the sate of the art in data analysis tasks including social networks and material science. We also test our method on node classification from the human-SARS CoV-2 protein-protein interactome.
