LATTE: Application Oriented Social Network Embedding

Abstract

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node will be represented as a feature vector. However, due to the detachment of the embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection, network alignment or information diffusion. In this paper, we propose to study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, we propose a novel network embedding framework, namely “app$\underline{L} ic \underline{A}$tion orien$\underline{T} ed ne \underline{T}$work$\underline{E}$mbedding” (LATTE). In LATTE, the heterogeneous network structure can be applied to compute the node “diffusive proximity” scores, which capture both the local and global network structures. Based on these computed scores, LATTE learns the network representation feature vectors by extending the autoencoder model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets with community detection as an example task, and the experimental results have demonstrated the outstanding performance of LATTE. Experiental results on other tasks are provided in the full-version of this paper at [16].