Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm
Siheng Chen, Sufeng Niu, Leman Akoglu, Jelena Kova\v{c}evi\'c, and Christos Faloutsos

TL;DR
This paper introduces GEM-D, a unifying framework for graph embedding algorithms, and proposes UltimateWalk, a scalable, parameter-free method that outperforms existing algorithms like DeepWalk and node2vec.
Contribution
The paper unifies existing graph embedding methods under GEM-D, introduces a novel warping function, and presents UltimateWalk, a one-click, scalable algorithm with superior performance.
Findings
GEM-D effectively unifies past graph embedding algorithms.
The exponential warping function empirically yields the best embeddings.
UltimateWalk outperforms DeepWalk and node2vec in experiments.
Abstract
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Caching and Content Delivery
