2017/02/19 by Siheng Chen, Chen, Siheng, Sufeng Niu +7 · 3 citations
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Caching and Content Delivery #Complex Network Analysis Techniques #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.1702.05764
arxiv created 2017/02/19 · openalex publication_date 2017/02/19 · arxiv updated 2017/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
What is the best way to describe a user in a social network with just a few numbers? Mathematically, this is equivalent to assigning a vector representation to each node in a graph, a process called graph embedding. We propose a novel framework, GEM-D that unifies most of the past algorithms such as LapEigs, DeepWalk and node2vec. GEM-D achieves its goal by decomposing any graph embedding algorithm into three building blocks: node proximity function, warping function and loss function. Based on thorough analysis of GEM-D, we propose a novel algorithm, called UltimateWalk, which outperforms the most-recently proposed state-of-the-art DeepWalk and node2vec. The contributions of this work are: (1) The proposed framework, GEM-D unifies the past graph embedding algorithms and provides a general recipe of how to design a graph embedding; (2) the nonlinearlity in the warping function contributes significantly to the quality of embedding and the exponential function is empirically optimal; (3) the proposed algorithm, UltimateWalk is one-click (no user-defined parameters), scalable and has a closed-form solution.