TY - GEN
T1 - Representation learning for large-scale dynamic networks
AU - Yu, Yanwei
AU - Yao, Huaxiu
AU - Wang, Hongjian
AU - Tang, Xianfeng
AU - Li, Zhenhui
N1 - Funding Information:
Acknowledgments. This work is partially supported by the National Natural Science Foundation of China under grant Nos. 61773331 and 61403328, the National Science Foundation under grant Nos. 1544455, 1652525, and 1618448, and the China Scholarship Council under grant No. 201608370018.
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Representation leaning on networks aims to embed networks into a low-dimensional vector space, which is useful in many tasks such as node classification, network clustering, link prediction and recommendation. In reality, most real-life networks constantly evolve over time with various kinds of changes to the network structure, e.g., creation and deletion of edges. However, existing network embedding methods learn the representation vectors for nodes in a static manner, which are not suitable for dynamic network embedding. In this paper, we propose a dynamic network embedding approach for large-scale networks. The method incrementally updates the embeddings by considering the changes of the network structures and is able to dynamically learn the embedding for networks with millions of nodes within a few seconds. Extensive experimental results on three real large-scale networks demonstrate the efficiency and effectiveness of our proposed methods.
AB - Representation leaning on networks aims to embed networks into a low-dimensional vector space, which is useful in many tasks such as node classification, network clustering, link prediction and recommendation. In reality, most real-life networks constantly evolve over time with various kinds of changes to the network structure, e.g., creation and deletion of edges. However, existing network embedding methods learn the representation vectors for nodes in a static manner, which are not suitable for dynamic network embedding. In this paper, we propose a dynamic network embedding approach for large-scale networks. The method incrementally updates the embeddings by considering the changes of the network structures and is able to dynamically learn the embedding for networks with millions of nodes within a few seconds. Extensive experimental results on three real large-scale networks demonstrate the efficiency and effectiveness of our proposed methods.
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U2 - 10.1007/978-3-319-91458-9_32
DO - 10.1007/978-3-319-91458-9_32
M3 - Conference contribution
AN - SCOPUS:85048973692
SN - 9783319914572
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 526
EP - 541
BT - Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
A2 - Pei, Jian
A2 - Sadiq, Shazia
A2 - Li, Jianxin
A2 - Manolopoulos, Yannis
PB - Springer Verlag
T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
Y2 - 21 May 2018 through 24 May 2018
ER -