Summary of Anomalyllm: Few-shot Anomaly Edge Detection For Dynamic Graphs Using Large Language Models, by Shuo Liu et al.
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
by Shuo Liu, Di Yao, Lanting Fang, Zhetao Li, Wenbin Li, Kaiyu Feng, XiaoWen Ji, Jingping Bi
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel method called AnomalyLLM is proposed for detecting anomaly edges in dynamic graphs. This problem is particularly challenging due to the emergence of new types of anomaly edges and limited labeled data for each type. Existing methods either focus on randomly inserted edges or require sufficient labeled data, hindering their applicability in real-world scenarios. To address this issue, AnomalyLLM leverages large language models (LLMs) and incorporates a dynamic-aware encoder to generate edge representations. The approach also employs an in-context learning framework that integrates information from a few labeled samples for few-shot anomaly detection. Experimental results on four datasets demonstrate the effectiveness of AnomalyLLM in improving few-shot anomaly detection performance, as well as achieving superior results on new anomalies without updating model parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly edges are important to detect in dynamic graphs because they can indicate unusual patterns or suspicious behavior. For example, this could be useful in cybersecurity to identify potential attacks or in financial transactions to detect fraudulent activity. Currently, there are limited methods that can effectively detect anomaly edges, especially when there is a lack of labeled data for each type of edge. A new approach called AnomalyLLM uses large language models and dynamic-aware encoders to generate representations of edges. This allows the model to learn from a few labeled samples and make accurate predictions about new anomalies. |
Keywords
» Artificial intelligence » Anomaly detection » Encoder » Few shot
