TY - GEN
T1 - GraRep
T2 - 24th ACM International Conference on Information and Knowledge Management, CIKM 2015
AU - Cao, Shaosheng
AU - Lu, Wei
AU - Xu, Qiongkai
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/10/17
Y1 - 2015/10/17
N2 - In this paper, we present GraRep, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the Deep-Walk model of Perozzi et al. [20] as well as the skip-gram model with negative sampling of Mikolov et al. [18] We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.
AB - In this paper, we present GraRep, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the Deep-Walk model of Perozzi et al. [20] as well as the skip-gram model with negative sampling of Mikolov et al. [18] We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.
KW - Dimension reduction
KW - Feature learning
KW - Graph representation
KW - Matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=84958239002&partnerID=8YFLogxK
U2 - 10.1145/2806416.2806512
DO - 10.1145/2806416.2806512
M3 - Conference contribution
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 891
EP - 900
BT - CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery (ACM)
Y2 - 19 October 2015 through 23 October 2015
ER -