Community detection is designed to divide a network into multiple subnetworks (communities) with high cohesiveness, which has attracted wide attention in graph analysis. Attributes are typically served as auxiliary side information to improve the ...

The goal of attributed community detection is to search a partition of the network such that there is high cohesion within each group and low coupling between two groups. We argue that multiple partitions of the attributed network should be ...

A new random vector functional link (RVFL) network with subspace-based local connections (abbreviated as RVFL-SLC network) is proposed in this paper. The main innovation of RVFL-SLC network is that the local connections are utilized in the input-...

Classical community search methods aim to detect local communities containing a set of sample nodes provided by users, which have been wildly studied in recent years. Existing efforts on community search have mainly detected ...

In this paper, we propose a community search scheme via attribute subspace. This method utilizes not only network structure but also node attributes within a certain subspace to quantify a community from the perspective of both internal ...

Outlier detection has important applications in many fields in which the data can contain high dimensions. However, finding the intentional knowledge of outliers will become inefficient and even infeasible in high dimensional space. In this paper, we ...