Graph Applications

We are committed to developing new algorithms and models, drawing from a wealth of insights gained through hands-on experience with real-world applications. Here are some practical use cases where graph neural networks and graph algorithms can be effectively applied: detecting financial crimes, predicting market trends, analyzing flight changes during the COVID-19 pandemic, and examining billion-scale social network dynamics.

Finance: Graph Neural Networks To Mitigate Financial Crime Risks (NeurIPS Workshop'19)

Financial crime is a large and growing problem, in some way touching almost every financial institution. Financial institutions are the front line in the war against financial crime and accordingly, must devote substantial human and technology resources to this effort. Current processes to detect financial misconduct have limitations in their ability to effectively differentiate between malicious behavior and ordinary financial activity. These limitations tend to result in gross over-reporting of suspicious activity that necessitate time-intensive and costly manual review. Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms. Where financial institutions address financial crimes through the lens of their own firm, perpetrators may devise sophisticated strategies that may span across institutions and geographies. Financial institutions continue to work relentlessly to advance their capabilities, forming partnerships across institutions to share insights, patterns and capabilities. These public-private partnerships are subject to stringent regulatory and data privacy requirements, thereby making it difficult to rely on traditional technology solutions. In this paper, we propose a methodology to share key information across institutions by using a federated graph learning platform that enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrated that our federated model outperforms local model by 20% with the UK FCA TechSprint data set. This new platform opens up a door to efficiently detecting global money laundering activity.

• Toyotaro Suzumura, Yi Zhou, Nathalie Barcardo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran: Towards Federated Graph Learning for Collaborative Financial Crimes Detection. NeurIPS Finance Workshop 2019

• Lucia Larise Stavarache, Donatas Narbutis, Toyotaro Suzumura, Ray Harishankar, Augustas Zaltauskas: Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincaré Embeddings. CoRR abs/1912.07701 (2019), NeurIPS Finance Workshop 2019
  
  

Graph Neural Networks for Market Prediction (NeurIPS 2018) 

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. The main goal of this work is to test the validity of this approach across different markets and longer time horizons for backtesting using rolling window analysis. In this work, we concentrate on the prediction of individual stock prices in the Japanese Nikkei 225 market over a period of roughly 20 years. For the knowledge graph, we use the Nikkei Value Search data, which is a rich dataset showing mainly supplier relations among Japanese and foreign companies. Our preliminary results show a 29.5% increase and a 2.2-fold increase in the return ratio and Sharpe ratio, respectively, when compared to the market benchmark, as well as a 6.32% increase and 1.3-fold increase, respectively, compared to the baseline LSTM model.

Papers
- Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis, NeurIPS 2018

The Impact of COVID-19 on Flight Networks (IEEE BigData 2020)

Paper: https://ieeexplore.ieee.org/document/9378218, IEEE  BigData 2020
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections. Such restrictions have a direct effect on the volume of international flights among these countries, resulting in extensive social and economic costs. To better understand the situation in a quantitative manner, we analyzed the OpenSky Network data to clarify flight patterns and flight densities around the world. Then we observed relationships between flight numbers with new infection cases and the economy (the unemployment rate) in Barcelona. We found that the number of daily flights gradually decreased and then suddenly dropped 64% during the second half of March in 2020 after the United States an

Billion-Scale Social Network Analytics (WWW 2012)  

Social network services such as Twitter, Facebook, MySpace, LinkedIn have been remarkably growing. There are various studies about social networks analysis. Haewoon Kwak performed the analysis of the Twitter network on 2009 and shows the degree of separation. However, the number of users on 2009 is about 41.7 million, the graph scale is not very large compared with the current graph. In this paper, we conduct a Twitter network analysis in terms growth by region, scale-free, reciprocity, degree of separation and diameter using Twitter user data with 469.9 million users and 28.7 billion relationships. We report that the value of degree of separation is 4.59 in current Twitter network through our experiments

Masaru Watanabe, Toyotaro Suzumura: How social network is evolving?: a preliminary study on billion-scale twitter network. WWW (Companion Volume) 2013: 531-534