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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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121242362483 · May 202619922001200920172026
48 results for core-periphery structure

The study shows portfolios based on core-periphery stock structure outperform traditional strategies.

problem Optimizing stock portfolios using mesoscale structures.
method Constructing portfolios based on the core-periphery profile of stocks from Pearson correlations.
result Portfolios based on the core-periphery profile of stocks outperform traditional strategies.

Paper introduces a core-periphery model for identifying informative network structures.

problem Noise and bias in non-informative periphery structures obscure the informative core in complex networks.
method Spectral algorithms for core identification as a preprocessing step for network analysis.
result The proposed method outperforms traditional core-periphery methods in various downstream tasks.

New algorithm detects cores in graphs with community structure, improving vertex selection for better clustering.

problem Understanding and detecting core-periphery structures in graphs with community structure.
method Introduces relative centrality to detect cores in graphs with community and core-periphery structures.
result Relative centrality solves bias issues in core detection, leading to better vertex selection and improved clustering performance.

Recent research on Bitcoin Transaction Networks reveals a growing, sparse, and core-periphery structure.

problem Understanding the evolution of Bitcoin's network structure and user behavior.
method Review of recent results on Bitcoin Transaction Networks, including Address Network, User Network, and Lightning Network.
result Bitcoin Transaction Networks exhibit a core-periphery structure, indicating increasing centralization.

Model analyzes optimal interbank networks during liquidity shocks, revealing core-periphery structures and co-investment requirements.

problem Formation of optimal interbank networks during liquidity shocks.
method Solves system-wide optimal control problem in two settings: decentralized and centralized.
result Decentralized setting leads to less cash reserves and greater vulnerability to shocks; core banks have highest co-investment requirements.

Spectral denoising recovers meaningful network structure from noisy financial correlations.

problem Noise in empirical correlation matrices from financial returns obscures genuine interactions.
method Spectral decomposition to separate structured and random components.
result Structured networks derived from 10-16 eigenmodes exhibit stronger core-periphery organization and scale-free degree distributions.

Study examines financial market structure changes during the COVID-19 crash using a novel MI approach.

problem Analyzing nonlinear dependencies among major stocks during market crashes.
method Conditional p-threshold mutual information (MI) and Minimum Spanning Tree (MST) framework.
result Financial networks become more integrated during crashes, with increased periphery vulnerability.

Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.

problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.

Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based on their spectral embedding - is commonly approached via K-means (or, more generally, Gaussian mixture model) clustering composed with either…

2018-08-23abs ↗pdf ↗

We show that the emergence of systemic risk in complex systems can be understood from the evolution of functional networks representing interactions inferred from fluctuation correlations between macroscopic observables. Specifically, we analyze the long-term collective dynamics of the New York Stock Exchange between 1…

2018-07-09abs ↗pdf ↗

Bayesian method detects mesoscale structures in pathway data networks.

problem Mesoscale structures in pathway data networks are hard to detect due to dependencies between interactions.
method Bayesian approach modeling optimal partitioning and higher-order dynamics.
result Method can recover both proximity-based and role-based groupings of nodes.

Analyzes how financial network dependencies can lead to multiple equilibrium outcomes and optimal bailout strategies.

problem Multiple equilibrium outcomes in financial networks due to dependency cycles.
method Characterized necessary and sufficient conditions for bank solvency, and provided upper bounds on optimal bailout payments.
result Minimum bailout payments needed to ensure systemic solvency and prevent cascading defaults.

Mesoscopic pattern extraction (MPE) is the problem of finding a partition of the nodes of a complex network that maximizes some objective function. Many well-known network inference problems fall in this category, including, for instance, community detection, core-periphery identification, and imperfect graph coloring.…

2018-06-11abs ↗pdf ↗

Many approaches have been proposed to discover clusters within networks. Community finding field encompasses approaches which try to discover clusters where nodes are tightly related within them but loosely related with nodes of other clusters. However, a community network configuration is not the only possible latent …

2019-07-27abs ↗pdf ↗

We consider a banking network represented by a system of stochastic differential equations coupled by their drift. We assume a core-periphery structure, and that the banks in the core hold a bubbly asset. The banks in the periphery have not direct access to the bubble, but can take initially advantage from its increase…

2018-06-05abs ↗pdf ↗

Method reconstructs financial networks from aggregate data, revealing critical link density.

problem Reconstructing financial networks from aggregate data is challenging due to unreconstructability phases.
method Random graph generation with desired link density and replicated constraints.
result There is a critical link density below which networks become unreconstructable.

The paper models reciprocity in interbank markets using a statistical null model.

problem Understanding the importance of individual banks in financial networks.
method Developed an exponential random graph model to account for reciprocal links on both topological and weighted levels.
result Weighted reciprocity in interbank markets is more significant than network size and volume before the financial crisis.

Unified framework detects dynamic community structure in brain networks across individuals.

problem Detecting community structure in functional brain networks across multiple subjects and over time.
method Markov-switching stochastic block model (MSS-SBM) for multilayer brain networks.
result Captures dynamic reconfiguration of modular connectivity in brain networks across different task conditions.

New model detects hidden group structures in criminal networks.

problem Challenges in identifying group structures in criminal networks with noisy data.
method Developed an extended stochastic block model (ESBM) to infer group structures.
result Unveiled complex block structures in an Italian mafia network.

In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes. This results in a network dataset containing the core nodes along with a potentially much larger set of fringe nodes that all have at least one interaction with a core node. In many settings, t…

2019-05-14abs ↗pdf ↗

One of the most defining features of the global financial network is its inherent complex and intertwined structure. From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic met…

2018-03-21abs ↗pdf ↗

Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very difficult…

2010-06-01abs ↗pdf ↗

A typical way in which network data is recorded is to measure all the interactions among a specified set of core nodes; this produces a graph containing this core together with a potentially larger set of fringe nodes that have links to the core. Interactions between pairs of nodes in the fringe, however, are not recor…

2018-05-03abs ↗pdf ↗

This study compares decentralized banks and finds some lack decentralization.

problem Decentralized banks do not fully decentralize transactions as expected.
method Network analysis of transaction data from four banks using core-periphery features.
result MakerDao and Compound are more decentralized than Aave and Liquity.

Regulator allocates buffers to prevent financial contagion in networks with common assets.

problem Containment of default contagion in financial networks with common asset exposures.
method Allocates nonnegative buffer vectors under linear budget constraints to maximize default or insolvency resilience margins or minimize worst-case systemic losses.
result Exact synthesis results for buffer allocation under \ell_{\infty} and 1\ell_{1} uncertainty sets, showing significant gains over uniform and exposure-proportional allocations.

The paper introduces new structures for left-symmetric algebroids.

problem Developing new mathematical structures for left-symmetric algebroids.
method Introducing Koszul-Vinberg-Nijenhuis structures and related concepts.
result Koszul-Vinberg-Nijenhuis structures provide a hierarchy of structures.

We give a notion of compatibility between a Riemannian structure and a Jacobi structure. We prove that in case of fundamental examples of Jacobi structures : Poisson structures, contact structures and locally conformally symplectic structures, we get respectively Riemann-Poisson structures in the sense of M. Boucetta, …

2018-02-25abs ↗pdf ↗

We give a notion of compatibility between a Riemannian metric and a Jacobi structure. We prove that in case of Poisson structures, contact structures and locally conformally symplectic structures, fundamental examples of Jacobi structures, we get respectively Riemann-Poisson structures in the sense of M. Boucetta, $\fr…

2017-08-14abs ↗pdf ↗

Study on G2G_2^* structures and almost para-contact structures in 7D.

problem Understanding the relation between G2G_2^* structures and almost para-contact structures.
method Calculating projections using properties of G2G_2^* structures.
result Determined the class of almost para-contact structures induced by G2G_2^* structures.

Defines a new Poisson structure for generalized Sasakian spaces.

problem No specific problem stated; focuses on new structure definition.
method Defines a canonical Poisson structure on generalized contact metric spaces.
result Shows distinction between generalized Sasakian and coKähler structures.

Study on types of generalized hypercomplex structures on tori and Kodaira-Thurston surface.

problem Characterizing types of generalized hypercomplex structures.
method Analysis of S2S^2-family of generalized complex structures and study of twistor spaces.
result Existence of generalized hypercomplex structures on 4n4n-dimensional tori with non-maximal types.

Extends corner structure study to general case, constructs normal Trans-Sasakian structures.

problem Extending corner structure study to general case without conditions.
method Extends corner structure to general case, constructs Trans-Sasakian structures from non-normal corner structures.
result Constructs normal Trans-Sasakian structures from non-normal corner structures.

New metric structures generalize Sasakian and cosymplectic structures, proving rigidity and finding conditions.

problem Generalizing Sasakian and cosymplectic structures to new metric structures.
method Introducing weak structures and proving rigidity of Sasakian structures.
result Any weak Sasakian structure is homothetically equivalent to a Sasakian structure.