Community Aware Random Walk for Network Embedding

TL;DR

The paper introduces CARE, a novel network embedding algorithm that incorporates community information and local structures, improving scalability and performance in social network analysis tasks like classification and link prediction.

Contribution

CARE uniquely combines local and community information in network embedding, handling weighted, directed, and complex networks with improved scalability and efficiency.

Findings

Outperforms existing methods in classification and link prediction tasks.

Effective in handling weighted, directed, and complex networks.

Scalable with incremental node addition and parallelized random walk generation.

Abstract

Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network. In this paper, we introduce a novel algorithm named "CARE" for network embedding that can be used for different types of networks including weighted, directed and complex. Current methods try to preserve local neighborhood information of nodes, whereas the proposed method utilizes local neighborhood and community information of network nodes to cover both local and global structure of social networks. CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. Subsequently, stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes. Our method can be scalable when new nodes are appended to network without information loss. Parallelize generation of customized random walks is also used for speeding up CARE. We evaluate the performance of CARE on multi label classification and link prediction tasks. Experimental results on various networks indicate that the proposed method outperforms others in both Micro and Macro-f1 measures for different size of training data.