TransNet: Translation-Based Network Representation Learning for Social
Relation Extraction
Abstract
Conventional network representation learning
(NRL) models learn low-dimensional vertex
representations by simply regarding each edge
as a binary or continuous value. However, there
exists rich semantic information on edges and
the interactions between vertices usually preserve
distinct meanings, which are largely neglected
by most existing NRL models. In this work, we
present a novel Translation-based NRL model,
TransNet, by regarding the interactions between
vertices as a translation operation. Moreover, we
formalize the task of Social Relation Extraction
(SRE) to evaluate the capability of NRL methods on modeling the relations between vertices.
Experimental results on SRE demonstrate that
TransNet significantly outperforms other baseline
methods by 10% to 20% on hits@1. The source
code and datasets can be obtained from https:
//github.com/thunlp/TransNet