Modularity-based Representation Learning for Networks

Network embedding aims at learning low-dimensional representation of vertexes in a network and effectively preserving network structures. These representations can be used as features for many complex tasks on networks such as community detection and multi-label classification. Some classic methods based on the skip-gram model have been proposed to learn the representation of vertexes. However, these methods do not consider the global structure（i.e., community structure） while sampling vertex sequences in network. To solve this problem, we suggest a novel sampling method which takes community information into consideration. It first samples dense vertex sequences by taking advantage of modularity function and then learns vertex representation by using the skip-gram model. Experimental results on the tasks of community detection and multi-label classification show that our method outperforms three state-of-the-art methods on learning the vertex representations in networks.