# Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms

arXiv: https://arxiv.org/abs/2301.07791 · HTML: https://mlsanity.com/paper/2301.07791/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Exploring the structure and function of temporal networks with dynamic graphlets](https://mlsanity.com/paper/1412.3885/) (2014)
- [Temporal Network Motifs: Models, Limitations, Evaluation](https://mlsanity.com/paper/2005.11817/) (2020)
- [Temporal motifs reveal homophily, gender-specific patterns and group talk in mobile communication networks](https://mlsanity.com/paper/1302.2563/) (2013)
- [Flow Motifs in Interaction Networks](https://mlsanity.com/paper/1810.08408/) (2018)
- [Dynamic networks reveal key players in aging](https://mlsanity.com/paper/1307.3388/) (2013)
- [Statistically validated mobile communication networks: Evolution of motifs in European and Chinese data](https://mlsanity.com/paper/1403.3785/) (2014)
- [Temporal Motifs in Patent Opposition and Collaboration Networks](https://mlsanity.com/paper/2110.11198/) (2021)
- [Detecting problematic transactions in a consumer-to-consumer e-commerce network](https://mlsanity.com/paper/1906.07974/) (2019)

## Built on by (most cited first, 3 of 3)

- [Using Motif Transitions for Temporal Graph Generation](https://mlsanity.com/paper/2306.11190/) (2023)
- [LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs](https://mlsanity.com/paper/2512.22266/) (2025)
- [ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks](https://mlsanity.com/paper/2508.20829/) (2025)

## Abstract (arXiv)

Understanding the dynamics of financial transactions among people is critical for various applications such as fraud detection. One important aspect of financial transaction networks is temporality. The order and repetition of transactions can offer new insights when considered within the graph structure. Temporal motifs, defined as a set of nodes that interact with each other in a short time period, are a promising tool in this context. In this work, we study three unique temporal financial networks: transactions in Mercari, an online marketplace, payments in a synthetic network generated by J.P. Morgan Chase, and payments and friendships among Venmo users. We consider the fraud detection problem on the Mercari and J.P. Morgan Chase networks, for which the ground truth is available. We show that temporal motifs offer superior performance to several baselines, including a previous method that considers simple graph features and two node embedding techniques (LINE and node2vec), while being practical in terms of runtime performance. For the Venmo network, we investigate the interplay between financial and social relations on three tasks: friendship prediction, vendor identification, and analysis of temporal cycles. For friendship prediction, temporal motifs yield better results than general heuristics, such as Jaccard and Adamic-Adar measures. We are also able to identify vendors with high accuracy and observe interesting patterns in rare motifs, such as temporal cycles. We believe that the analysis, datasets, and lessons from this work will be beneficial for future research on financial transaction networks.