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A0700
Title: Signed network embedding and its applications to detection of communities and anomalies Authors:  Junhui Wang - Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: Signed networks are frequently observed in real life with additional sign information associated with each edge, yet such information has been largely ignored in existing network models. We will introduce a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downstream analysis, including community detection, anomaly detection, and network inference. The proposed model captures both balance structure and anomaly effect through a low rank plus sparse matrix decomposition, which are jointly estimated via a regularized formulation. Its theoretical properties will be discussed in terms of asymptotic consistency and finite-sample probability bounds for network embedding, community detection and anomaly detection. The advantage of the proposed embedding model is also demonstrated through extensive numerical experiments on both synthetic networks and an international relation network.