BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
Abstract
Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into low-dimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely 'Deep alIgned autoencoder based eMbEdding' (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors.
Recommended Citation
J. Zhang et al., "BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder," Proceedings of the 2017 IEEE International Conference on Data Mining (2017, New Orleans, LA), pp. 605 - 614, Institute of Electrical and Electronics Engineers (IEEE), Nov 2017.
The definitive version is available at https://doi.org/10.1109/ICDM.2017.70
Meeting Name
2017 IEEE International Conference on Data Mining, ICDM 2017 (2017: Nov. 18-21, New Orleans, LA)
Department(s)
Computer Science
Keywords and Phrases
Artificial intelligence; Data mining; Digital storage; Information services; Learning algorithms; Learning systems; Social networking (online); Vector spaces; Attribute information; Broad Learning; Feature representation; Heterogeneous information; Heterogeneous links; Network embedding; Network structures; On-line applications; Complex networks; Heterogeneous Information Network; Multiple Aligned Social Networks
International Standard Book Number (ISBN)
978-1-5386-3835-4
International Standard Serial Number (ISSN)
1550-4786; 2374-8486
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Nov 2017
Comments
This work is supported in part by NSF through grants IIS-1526499, and CNS-1626432, and NSFC 61672313. This work was also partially supported by University of Missouri Research Board (UMRB) via the proposal number: 4991.