English

Symmetrization for Embedding Directed Graphs

Social and Information Networks 2018-11-30 v1 Artificial Intelligence Machine Learning

Abstract

Recently, one has seen a surge of interest in developing such methods including ones for learning such representations for (undirected) graphs (while preserving important properties). However, most of the work to date on embedding graphs has targeted undirected networks and very little has focused on the thorny issue of embedding directed networks. In this paper, we instead propose to solve the directed graph embedding problem via a two-stage approach: in the first stage, the graph is symmetrized in one of several possible ways, and in the second stage, the so-obtained symmetrized graph is embedded using any state-of-the-art (undirected) graph embedding algorithm. Note that it is not the objective of this paper to propose a new (undirected) graph embedding algorithm or discuss the strengths and weaknesses of existing ones; all we are saying is that whichever be the suitable graph embedding algorithm, it will fit in the above proposed symmetrization framework.

Keywords

Cite

@article{arxiv.1811.12164,
  title  = {Symmetrization for Embedding Directed Graphs},
  author = {Jiankai Sun and Srinivasan Parthasarathy},
  journal= {arXiv preprint arXiv:1811.12164},
  year   = {2018}
}

Comments

has been accepted to The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019) Student Abstract and Poster Program

R2 v1 2026-06-23T06:25:11.267Z