Tootfinder

trec: TREC collection (2010)
A bipartite network of documents and the words they contain, extracted from NIST's Text Retrieval Conference (TREC) disks 4 and 5, from 2010. These archives contain material drawn from the Financial Times Ltd., the Congressional Record of the 103rd Congress, the Federal Register, the Foreign Broadcast Information Service, and the Los Angeles Times newspaper.
This network has 1729302 nodes and 83629405 edges.
Tags: Informational, Language, Un…

This https://arxiv.org/abs/2410.02544 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Federated k-Core Decomposition: A Secure Distributed Approach
As one of the most well-studied cohesive subgraph models, the $k$-core is widely used to find graph nodes that are ``central'' or ``important'' in many applications, such as biological networks, social networks, ecological networks, and financial networks. For Decentralized Online Social Networks (DOSNs), where each vertex is a client as a single computing unit, distributed k-core decomposition algorithms have already been proposed. However, current distributed approaches fail to adequately pro…

trec: TREC collection (2010)
A bipartite network of documents and the words they contain, extracted from NIST's Text Retrieval Conference (TREC) disks 4 and 5, from 2010. These archives contain material drawn from the Financial Times Ltd., the Congressional Record of the 103rd Congress, the Federal Register, the Foreign Broadcast Information Service, and the Los Angeles Times newspaper.
This network has 1729302 nodes and 83629405 edges.
Tags: Informational, Language, Un…

Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations
Linh Nguyen, Marcel Boersma, Erman Acar
https://arxiv.org/abs/2507.01980

Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations
Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzing large volumes of data. While machine learning (ML) approaches seem like a viable solution, applying them successfully is not so easy due to two main challenges: (1) the sparsely labeled data, which makes the training of such approaches challenging (with inherent labeling costs), and (2) lack of explainability for th…

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
Christian Franssen, Iman van Lelyveld, Bernd Heidergott
https://arxiv.org/abs/2507.00600

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning. We propose an interpretable role-based clustering approach for multi-layer financial networks, designed to identify the functional positions of institutions across different market segments. Our method follows a general clustering framework defined by proximity measures, cluster evaluation criteria, and algorithm sel…

This https://arxiv.org/abs/2407.14272 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Global Balance and Systemic Risk in Financial Correlation Networks
The global balance index is used in the network literature to quantify how balanced a signed network is. In this paper we show that the global balance index of financial correlation networks can be used as a systemic risk measure. We define the global balance index of a network starting from a diffusive process that describes how the information spreads across nodes in a network, providing an alternative derivation to the usual combinatorial one. The steady state of this process is the solution…

This https://arxiv.org/abs/2311.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud detection for financial services. At the heart of these networks are the edges, which are crucial in guiding GNN models' predictions. In many scenarios, these edges represent sensitive information, such as personal associations or financial dealings, which require privacy assurance. However, their contributions to GNN mo…

Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
Nurbanu Bursa
https://arxiv.org/abs/2506.01945

Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and Türkiye), are gaining influence in global stock markets, although they remain susceptible to the economic conditions of developed countries like the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This interconnectedness and sensitivity of financial markets make understanding these relationships crucial for investors and policymakers to predict stock price movements accur…

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
Andrew Cornfeld, Ashley Miller, Mercedes Mora-Figueroa, Kurt Samuels, Anthony Palomba
https://arxiv.org/abs/2506.00076

Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
Television networks face high financial risk when making programming decisions, often relying on limited historical data to forecast episodic viewership. This study introduces a machine learning framework that integrates natural language processing (NLP) features from over 25000 television episodes with traditional viewership data to enhance predictive accuracy. By extracting emotional tone, cognitive complexity, and narrative structure from episode dialogue, we evaluate forecasting performance…

Deep Learning Enhanced Multivariate GARCH
Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, Chao Wang
https://arxiv.org/abs/2506.02796 https://…

Deep Learning Enhanced Multivariate GARCH
This paper introduces a novel multivariate volatility modeling framework, named Long Short-Term Memory enhanced BEKK (LSTM-BEKK), that integrates deep learning into multivariate GARCH processes. By combining the flexibility of recurrent neural networks with the econometric structure of BEKK models, our approach is designed to better capture nonlinear, dynamic, and high-dimensional dependence structures in financial return data. The proposed model addresses key limitations of traditional multiva…

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

Geopolitical Tensions and Financial Networks: Strategic Shifts Toward Alternatives
Antonis Ballis
https://arxiv.org/abs/2505.21480 https://

Geopolitical Tensions and Financial Networks: Strategic Shifts Toward Alternatives
Global financial systems are undergoing strategic shifts as geopolitical tensions reshape international trade and payments. The United States (US)-China trade war, sanctions regimes, and rising concerns over the weaponization of financial infrastructures like SWIFT have led countries to seek alternative networks, including China's CIPS and emerging cross-border CBDCs. This letter presents a dynamic theoretical framework where sanction risks, investment choices, and network effects drive payment…

Optimal Regulation and Investment Incentives in Financial Networks
Matthew O. Jackson, Agathe Pernoud
https://arxiv.org/abs/2506.16648 https://

Optimal Regulation and Investment Incentives in Financial Networks
We examine optimal regulation of financial networks with debt interdependencies between financial firms. We first characterize when it is firms have an incentive to choose excessively risky portfolios and overly correlate their portfolios with those of their counterparties. We then characterize how optimal regulation depends on a firm's financial centrality and its available investment opportunities. In standard core-periphery networks, optimal regulation depends non-monotonically on the correl…

Covariate-informed link prediction with extreme taxonomic bias
Jennifer N. Kampe, Camille DeSisto, David B. Dunson
https://arxiv.org/abs/2506.23370 https:/…

Covariate-informed link prediction with extreme taxonomic bias
Biotic interactions provide a valuable window into the inner workings of complex ecological communities and capture the loss of ecological function often precipitated by environmental change. However, the financial and logistical challenges associated with collecting interaction data result in networks that are recorded with geographical and taxonomic bias, particularly when studies are narrowly focused. We develop an approach to reduce bias in link prediction in the common scenario in which da…

cofe: Collaboration in Financial Economics (2021)
Network of authors and commenters of papers in finantial economics, obtained from the acknowledgement sections of over 5,000 published research papers.
This network has 13967 nodes and 102039 edges.
Tags: Social, Directed, Weighted, Online, Collaboration
https://networks.ske…

This https://arxiv.org/abs/2309.15511 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Measuring risk contagion in financial networks with CoVaR
The stability of a complex financial system may be assessed by measuring risk contagion between various financial institutions with relatively high exposure. We consider a financial network model using a bipartite graph of financial institutions (e.g., banks, investment companies, insurance firms) on one side and financial assets on the other. Following empirical evidence, returns from such risky assets are modeled by heavy-tailed distributions, whereas their joint dependence is characterized b…

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Shunyao Wang, Ming Cheng, Christina Dan Wang
https://arxiv.org/abs/2505.06864

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Stochastic Discount Factor (SDF) models provide a unified framework for asset pricing and risk assessment, yet traditional formulations struggle to incorporate unstructured textual information. We introduce NewsNet-SDF, a novel deep learning framework that seamlessly integrates pretrained language model embeddings with financial time series through adversarial networks. Our multimodal architecture processes financial news using GTE-multilingual models, extracts temporal patterns from macroecono…

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu
https://arxiv.org/abs/2506.10399

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhea…

Boosting Binomial Exotic Option Pricing with Tensor Networks
Maarten van Damme, Rishi Sreedhar, Martin Ganahl
https://arxiv.org/abs/2505.17033 https://

Boosting Binomial Exotic Option Pricing with Tensor Networks
Pricing of exotic financial derivatives, such as Asian and multi-asset American basket options, poses significant challenges for standard numerical methods such as binomial trees or Monte Carlo methods. While the former often scales exponentially with the parameters of interest, the latter often requires expensive simulations to obtain sufficient statistical convergence. This work combines the binomial pricing method for options with tensor network techniques, specifically Matrix Product States…

trec: TREC collection (2010)
A bipartite network of documents and the words they contain, extracted from NIST's Text Retrieval Conference (TREC) disks 4 and 5, from 2010. These archives contain material drawn from the Financial Times Ltd., the Congressional Record of the 103rd Congress, the Federal Register, the Foreign Broadcast Information Service, and the Los Angeles Times newspaper.
This network has 1729302 nodes and 83629405 edges.
Tags: Informational, Language, Un…

Microgrids Coalitions for Energy Market Balancing
Viorica Chifu, Cristina Bianca Pop, Tudor Cioara, Ionut Anghel
https://arxiv.org/abs/2506.06058 https://

Microgrids Coalitions for Energy Market Balancing
With the integration of renewable sources in electricity distribution networks, the need to develop intelligent mechanisms for balancing the energy market has arisen. In the absence of such mechanisms, the energy market may face imbalances that can lead to power outages, financial losses or instability at the grid level. In this context, the grouping of microgrids into optimal coalitions that can absorb energy from the market during periods of surplus or supply energy to the market during perio…

trec: TREC collection (2010)
A bipartite network of documents and the words they contain, extracted from NIST's Text Retrieval Conference (TREC) disks 4 and 5, from 2010. These archives contain material drawn from the Financial Times Ltd., the Congressional Record of the 103rd Congress, the Federal Register, the Foreign Broadcast Information Service, and the Los Angeles Times newspaper.
This network has 1729302 nodes and 83629405 edges.
Tags: Informational, Language, Un…

SocialCredit
Thabassum Aslam, Anees Aslam
https://arxiv.org/abs/2506.12099 https://arxiv.org/pdf/2506.12099

SocialCredit+
SocialCredit+ is AI powered credit scoring system that leverages publicly available social media data to augment traditional credit evaluation. It uses a conversational banking assistant to gather user consent and fetch public profiles. Multimodal feature extractors analyze posts, bios, images, and friend networks to generate a rich behavioral profile. A specialized Sharia-compliance layer flags any non-halal indicators and prohibited financial behavior based on Islamic ethics. The platform emp…

trec: TREC collection (2010)
A bipartite network of documents and the words they contain, extracted from NIST's Text Retrieval Conference (TREC) disks 4 and 5, from 2010. These archives contain material drawn from the Financial Times Ltd., the Congressional Record of the 103rd Congress, the Federal Register, the Foreign Broadcast Information Service, and the Los Angeles Times newspaper.
This network has 1729302 nodes and 83629405 edges.
Tags: Informational, Language, Un…

trec: TREC collection (2010)
A bipartite network of documents and the words they contain, extracted from NIST's Text Retrieval Conference (TREC) disks 4 and 5, from 2010. These archives contain material drawn from the Financial Times Ltd., the Congressional Record of the 103rd Congress, the Federal Register, the Foreign Broadcast Information Service, and the Los Angeles Times newspaper.
This network has 1729302 nodes and 83629405 edges.
Tags: Informational, Language, Un…

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata

corporate_directors: Global corporate directors (2016)
Bipartite network of directors and the companies on whose boards they sit, spanning 54 countries worldwide, constructed from data collected by the Financial Times (c. Sept. 2016). Person nodes are annotated with age and gender. Company nodes are annotated with their country, sector, industry, and number of employees.
This network has 356638 nodes and 377060 edges.
Tags: Economic, Governance, Unweighted, Metadata