Active graph anomaly detection

Agarwal, Ishika

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

Description

Title

Active graph anomaly detection

Author(s)

Agarwal, Ishika

Issue Date

2024-04-09

Director of Research (if dissertation) or Advisor (if thesis)

Tong, Hanghang

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

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

Abstract

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.

Graduation Semester

2024-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2024 Ishika Agarwal

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