Fast Attributed Graph Embedding via Density of States

2021 IEEE International Conference on Data Mining (ICDM)(2021)

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摘要
Given a node-attributed graph, how can we efficiently represent it with few numerical features that expressively reflect its topology and attribute information? We propose A-DOGE, for Attributed DOS-based Graph Embedding, based on density of states (DOS, a.k.a. spectral density) to tackle this problem. A-DOGE is designed to fulfill a long desiderata of desirable characteristics. Most notably, it c...
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关键词
Runtime,Databases,Conferences,Approximation algorithms,Topology,Classification algorithms,Data mining
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