Active graph anomaly detection

Recently, detecting anomalies in attributed networks has gained a lot of attention from research communities due to the numerous real-world use cases in the financial, social media, medical, and agricultural domains. This thesis aims to explore node anomaly detection in two different aspects: soft-labeling, and multi-armed bandits. The environment in both settings is constrained to an active learning scenario where there is no direct access to ground truth labels but access to an oracle. This thesis comprises of three works: one using soft-labeling, another with multi-armed bandits, and a third that explores a combination of both. We present experimental results for each work to justify the algorithmic decisions that were made. Future work is also discussed to build on top of these methods.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Agarwal, Ishika

Contributors dc:contributor

• Tong, Hanghang

• Graph
• Anomaly Detection
• Machine Learning
• Reinforcement Learning
• Active Learning

Statement dc:rights

• Copyright 2024 Ishika Agarwal

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124244

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124244