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.
TransNet REF utilizes rich semantic information on edges and regard the interactions between nodes as translation operation to conduct network embedding.
TransNet: Translation-Based Network Representation Learning for Social Relation Extraction
IJCAI
Computer Science