# Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks

arXiv: https://arxiv.org/abs/2104.08637 · HTML: https://mlsanity.com/paper/2104.08637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [GraphSAC: Detecting anomalies in large-scale graphs](https://mlsanity.com/paper/1910.09589/) (2019)
- [Kernel-based Inference of Functions over Graphs](https://mlsanity.com/paper/1711.10353/) (2017)
- [Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection](https://mlsanity.com/paper/1809.08353/) (2018)
- [Efficient and Stable Graph Scattering Transforms via Pruning](https://mlsanity.com/paper/2001.09882/) (2020)
- [Joint community and anomaly tracking in dynamic networks](https://mlsanity.com/paper/1506.07611/) (2015)
- [Mask Combination of Multi-layer Graphs for Global Structure Inference](https://mlsanity.com/paper/1910.10114/) (2019)

## Abstract (arXiv)

Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, finance, security, to list a few. The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths, which can be easily distributed, and hence efficient. The first relies on decomposing the graph data matrix into low rank plus sparse components to markedly improve performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance. The novel methods not only capture anomalous edges linking nodes of different communities, but also spurious connections between any two nodes with different features. Experiments conducted on real and synthetic data corroborate the effectiveness of both methods in the anomaly identification task.