Deep core node information embedding on networks with missing edges for community detection

The incomplete network is defined as the network with missing edges, which forms incomplete network topology by missing real information because of multiple-factor such as personal privacy security and threats, etc. Academic interest in incomplete network studies is increasing. Some methods solving community detection problem in the incomplete network, as link prediction, show low ACC or NMI. To address those, there is a need for approaches less affected by missing edges and easy to obtain communities. We propose a deep core node information embedding(DCNIE) algorithm on network with missing edges for community detection, aiming to obtain core node information rather than the influence of edges. First, by edge augmentation, the network with missing edges is integrated into complete networks. Second, the k-core algorithm is used to obtain core node information and build a similarity matrix, followed by an unsupervised deep method that implements network embedding to obtain a low-dimensional feature matrix. Finally, Gaussian mixture model is used for clustering to obtain the community division. We compare eleven state-of-the-art methods on eleven real networks by using eight evaluation metrics. Experiments demonstrate that DCNIE is superior in performance and efficiency while gaining accurate community division in incomplete network.