# EdgeCentric: Anomaly Detection in Edge-Attributed Networks

arXiv: https://arxiv.org/abs/1510.05544 · HTML: https://mlsanity.com/paper/1510.05544/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2015.

## Built on by (most cited first, 9 of 9)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Sketch-Based Anomaly Detection in Streaming Graphs](https://mlsanity.com/paper/2106.04486/) (2021)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Graph Anomaly Detection with Unsupervised GNNs](https://mlsanity.com/paper/2210.09535/) (2022)
- [On the Detection of Reviewer-Author Collusion Rings From Paper Bidding](https://mlsanity.com/paper/2402.07860/) (2024)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection](https://mlsanity.com/paper/2510.26307/) (2025)

## Abstract (arXiv)

Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adjacent nodes interact with other entities in the network. In this paper, we aim to utilize exactly this information to discern suspicious from typical node behavior. Our work has a number of notable contributions, including (a) formulation: while most other graph-based anomaly detection works use structural graph connectivity or node information, we focus on the new problem of leveraging edge information, (b) methodology: we introduce EdgeCentric, an intuitive and scalable compression-based approach for detecting edge-attributed graph anomalies, and (c) practicality: we show that EdgeCentric successfully spots numerous such anomalies in several large, edge-attributed real-world graphs, including the Flipkart e-commerce graph with over 3 million product reviews between 1.1 million users and 545 thousand products, where it achieved 0.87 precision over the top 100 results.