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.