Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization

Exploiting Multi-layer Graph Factorization for Multi-attributed Graph Matching

Multi-attributed graph matching is a problem of finding correspondences between two sets of data while considering their complex properties described in multiple attributes. However, the information of multiple attributes is likely to be oversimplified during a process that makes an integrated attribute, and this degrades the matching accuracy. For that reason, a multi-layer graph structure-bas...

Community Detection in Multi-Layer Networks

Recent years have seen a surge of interest in community detection and a large range of algorithms have been designed and applied to this specific task. However, the vast majority of the work done in the area has been concerned with simple weighted graphs. This results in some of the real world systems being oversimplified to fit this model. In this project, I design a new community detection al...

Community Detection and Analysis on Attributed Social Networks

Approximate Nonnegative Matrix Factorization via Alternating Minimization

In this paper we consider the Nonnegative Matrix Factorization (NMF) problem: given an (elementwise) nonnegative matrix V ∈ R + find, for assigned k, nonnegative matrices W ∈ R + and H ∈ R k×n + such that V = WH . Exact, non trivial, nonnegative factorizations do not always exist, hence it is interesting to pose the approximate NMF problem. The criterion which is commonly employed is I-divergen...

Accelerated Local Anomaly Detection via Resolving Attributed Networks

Attributed networks, in which network connectivity and node attributes are available, have been increasingly used to model real-world information systems, such as social media and e-commerce platforms. While outlier detection has been extensively studied to identify anomalies that deviate from certain chosen background, existing algorithms cannot be directly applied on attributed networks due t...