TY - JOUR
T1 - Jointly learning representations of nodes and attributes for attributed networks
AU - Meng, Zaiqiao
AU - Liang, Shangsong
AU - Zhang, Xiangliang
AU - McCreadie, Richard
AU - Ounis, Iadh
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank the reviewers for their valuable comments
PY - 2020/1/27
Y1 - 2020/1/27
N2 - Previous embedding methods for attributed networks aim at learning low-dimensional vector representations only for nodes but not for both nodes and attributes, resulting in the fact that node embeddings cannot be directly used to recover the correlations between nodes and attributes. However, capturing such correlations by embeddings is of great importance for many real-world applications, such as attribute inference and user profiling. Moreover, in real-world scenarios, many attributed networks evolve over time, with their nodes, links, and attributes changing from time to time. In this article, we study the problem of jointly learning low-dimensional representations of both nodes and attributes for static and dynamic attributed networks. To address this problem, we propose a Co-embedding model for Static Attributed Networks (CSAN), which jointly learns low-dimensional representations of both attributes and nodes in the same semantic space such that their affinities can be effectively captured andmeasured, and a Co-embedding model for Dynamic Attributed Networks (CDAN) to dynamically track low-dimensional representations of nodes and attributes over time. To obtain effective embeddings, both our co-embedding models, CSAN and CDAN, embed each node and attribute with means and variances of Gaussian distributions via variational auto-encoders. Our CDAN model formulates the dynamic changes of a dynamic attributed network by aggregating perturbation features from the nodes' local neighborhoods as well as attributes' associations such that the evolving patterns of the given network can be tracked. Experimental results on real-world networks demonstrate that our proposed embedding models outperform state-of-the-art non-dynamic and dynamic embedding models.
AB - Previous embedding methods for attributed networks aim at learning low-dimensional vector representations only for nodes but not for both nodes and attributes, resulting in the fact that node embeddings cannot be directly used to recover the correlations between nodes and attributes. However, capturing such correlations by embeddings is of great importance for many real-world applications, such as attribute inference and user profiling. Moreover, in real-world scenarios, many attributed networks evolve over time, with their nodes, links, and attributes changing from time to time. In this article, we study the problem of jointly learning low-dimensional representations of both nodes and attributes for static and dynamic attributed networks. To address this problem, we propose a Co-embedding model for Static Attributed Networks (CSAN), which jointly learns low-dimensional representations of both attributes and nodes in the same semantic space such that their affinities can be effectively captured andmeasured, and a Co-embedding model for Dynamic Attributed Networks (CDAN) to dynamically track low-dimensional representations of nodes and attributes over time. To obtain effective embeddings, both our co-embedding models, CSAN and CDAN, embed each node and attribute with means and variances of Gaussian distributions via variational auto-encoders. Our CDAN model formulates the dynamic changes of a dynamic attributed network by aggregating perturbation features from the nodes' local neighborhoods as well as attributes' associations such that the evolving patterns of the given network can be tracked. Experimental results on real-world networks demonstrate that our proposed embedding models outperform state-of-the-art non-dynamic and dynamic embedding models.
UR - http://hdl.handle.net/10754/661660
UR - https://dl.acm.org/doi/10.1145/3377850
UR - http://www.scopus.com/inward/record.url?scp=85079431925&partnerID=8YFLogxK
U2 - 10.1145/3377850
DO - 10.1145/3377850
M3 - Article
SN - 1046-8188
VL - 38
SP - 1
EP - 32
JO - ACM Transactions on Information Systems
JF - ACM Transactions on Information Systems
IS - 2
ER -