# Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

arXiv: https://arxiv.org/abs/2211.13123 · HTML: https://mlsanity.com/paper/2211.13123/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction](https://mlsanity.com/paper/2109.11519/) (2021)

## Built on by (most cited first, 4 of 4)

- [TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support](https://mlsanity.com/paper/2306.13339/) (2023)
- [LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs](https://mlsanity.com/paper/2512.22266/) (2025)
- [Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation](https://mlsanity.com/paper/2304.05749/) (2023)
- [Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision](https://mlsanity.com/paper/2602.20019/) (2026)

## Abstract (arXiv)

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.