# Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

arXiv: https://arxiv.org/abs/2605.29526 · HTML: https://mlsanity.com/paper/2605.29526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 24 of 24)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Towards Better Dynamic Graph Learning: New Architecture and Unified Library](https://mlsanity.com/paper/2303.13047/) (2023)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Empowering Graph Representation Learning with Test-Time Graph Transformation](https://mlsanity.com/paper/2210.03561/) (2022)
- [MotifNet: a motif-based Graph Convolutional Network for directed graphs](https://mlsanity.com/paper/1802.01572/) (2018)
- [GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2306.12251/) (2023)
- [Conditional Negative Sampling for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2010.02037/) (2020)
- [ARC: A Generalist Graph Anomaly Detector with In-Context Learning](https://mlsanity.com/paper/2405.16771/) (2024)
- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts](https://mlsanity.com/paper/2410.14886/) (2024)
- [COLA: Cross-city Mobility Transformer for Human Trajectory Simulation](https://mlsanity.com/paper/2403.01801/) (2024)
- [Partitioning Message Passing for Graph Fraud Detection](https://mlsanity.com/paper/2412.00020/) (2024)
- [AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection](https://mlsanity.com/paper/2502.09254/) (2025)
- [Behavior-aware Account De-anonymization on Ethereum Interaction Graph](https://mlsanity.com/paper/2203.09360/) (2022)
- [SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels](https://mlsanity.com/paper/2502.03201/) (2025)
- [Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation](https://mlsanity.com/paper/2306.09381/) (2023)
- [GraphTTA: Test Time Adaptation on Graph Neural Networks](https://mlsanity.com/paper/2208.09126/) (2022)
- [Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation](https://mlsanity.com/paper/2304.07503/) (2023)
- [Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites](https://mlsanity.com/paper/2005.14440/) (2020)
- [Real-time intelligent big data processing: technology, platform, and applications](https://mlsanity.com/paper/2111.11872/) (2021)
- [When Crypto Economics Meet Graph Analytics and Learning](https://mlsanity.com/paper/2403.06454/) (2024)

## Built on by (most cited first, 1 of 1)

- [CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing](https://mlsanity.com/paper/2610.16449/) (2026)

## Abstract (arXiv)

Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: textitadversarial pattern evolution by malicious actors and textitthe out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains. To address these challenges, we propose a novel framework termed TEmporal Motif-aware Graph Test-Time Adaptation (TEMG-TTA). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed TEMG-TTA outperforms textitstate-of-the-art GAD approaches by an average of 54.88%. A further case study on interpretable motif patterns reveals that TEMG-TTA explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.