Title: Community Aware Random Walk for Network Embedding,
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.