LATTE: Application Oriented Network Embedding

In recent years, many research works propose to embed the networked data into a low-dimensional feature space, where each node is represented as a feature vector. With the embedding feature vectors, the original network structure can be e‚ectively reconstructed, classic learning algorithms can be applied directly, and more importantly the learned embedding representations can also be widely used in external applications. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ine‚ective for application tasks with speci€c objectives, e.g., community detection vs information di‚usion. In addition, the networked data has become more and more complicated nowadays, which can involve both heterogeneous structures and diverse aŠributes, and few existing homogeneous network embedding models can handle them well. In this paper, we will study the application oriented heterogeneous network embedding problem. Signi€cantly di‚erent from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external application in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the “appLicAtion orienTed neTwork Embedding” (Latte) model. In Latte, we introduce a new concept called “aŠributed heterogeneous social network” to model the diverse structure and aŠribute information available in the networks. Meanwhile, the heterogeneous network structure can be applied to compute the node “di‚usive proximity” score, which capture both local and global network structures. Furthermore, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also e‚ectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the application task oriented network embeddings. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro€t or commercial advantage and that copies bear this notice and the full citation on the €rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permiŠed. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci€c permission and/or a fee. Request permissions from [email protected]. Conference’17, Washington, DC, USA © 2016 ACM. 978-x-xxxx-xxxx-x/YY/MM. . .$15.00 DOI: 10.1145/nnnnnnn.nnnnnnn ACM Reference format: Jiawei Zhang?, Limeng Cui¶ , Yanjie Fu$ . 2016. LATTE: Application Oriented Network Embedding. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 10 pages. DOI: 10.1145/nnnnnnn.nnnnnnn