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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.

169,181 papers · 148 categories

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52103155206 · Jun 202019922001200920182026
48 results for cascade size

New research reveals diverse cascade sizes in finite networks, challenging traditional risk assessments.

problem Predicting the size of cascades in finite networks is difficult due to uncertain parameters and missing information.
method Derived explicit closed-form solutions for cascade size distributions in complete and star networks.
result Broad and even bimodal cascade size distributions in finite networks, not centered around the average.

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and c…

2014-03-18abs ↗pdf ↗

We analyze cascades of defaults in an interbank loan market. The novel feature of this study is that the network structure and the size distribution of banks are derived from empirical data. We find that the ability of a defaulted institution to start a cascade depends on an interplay of shock size and connectivity. Fu…

2013-10-06abs ↗pdf ↗

External or internal shocks may lead to the collapse of a system consisting of many agents. If the shock hits only one agent initially and causes it to fail, this can induce a cascade of failures among neighoring agents. Several critical constellations determine whether this cascade remains finite or reaches the size o…

2012-09-05abs ↗pdf ↗

We introduce a probabilistic framework that represents stylized banking networks with the aim of predicting the size of contagion events. Most previous work on random financial networks assumes independent connections between banks, whereas our framework explicitly allows for (dis)assortative edge probabilities (e.g., …

2016-10-13abs ↗pdf ↗

A probabilistic framework is introduced that represents stylized banking networks and aims to predict the size of contagion events. In contrast to previous work on random financial networks, which assumes independent connections between banks, the possibility of disassortative edge probabilities (an above average tende…

2011-10-19abs ↗pdf ↗

Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference in the exponentially-sized output spaces such models require. We formulate and develop the Structured Prediction Cascade architecture: a seque…

2012-08-06abs ↗pdf ↗

In an adaptive population which models financial markets and distributed control, we consider how the dynamics depends on the diversity of the agents' initial preferences of strategies. When the diversity decreases, more agents tend to adapt their strategies together. This change in the environment results in dynamical…

2006-09-26abs ↗pdf ↗

We study simultaneous price drops of real stocks and show that for high drop thresholds they follow a power-law distribution. To reproduce these collective downturns, we propose a minimal self-organized model of cascade spreading based on a probabilistic response of the system elements to stress conditions. This model …

2010-03-16abs ↗pdf ↗

We consider the problem of finding the graph on which an epidemic cascade spreads, given only the times when each node gets infected. While this is a problem of importance in several contexts -- offline and online social networks, e-commerce, epidemiology, vulnerabilities in infrastructure networks -- there has been ve…

2012-02-08abs ↗pdf ↗

A new approach reduces the size of prediction sets in CP, making it more efficient.

problem Large and costly prediction sets in standard CP.
method Expand correctness criterion to include inferred admissible answers, and use prediction cascades to prune implausible labels.
result Empirically effective in natural language processing and drug discovery.

Algorithm estimates clock in network cascades to improve performance.

problem Temporal distortion in cascade observation leads to performance degradation.
method Formulated clock estimation problem, developed FastClock algorithm.
result FastClock algorithm outperforms state-of-the-art in terms of accuracy and speed.

In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable …

2017-06-07abs ↗pdf ↗

Two algorithms improve performance in piecewise-stationary cascading bandits.

problem Real-world user preferences change over time, complicating stationary bandit models.
method Developed two algorithms, GLRT-CascadeUCB and GLRT-CascadeKL-UCB, using a change-point detector (GLRT).
result Regret upper bounds of O(NLTlogT)\mathcal{O}(\sqrt{NLT\log{T}}), improving on existing methods.

This paper optimizes power grid protection settings to maximize network degradation due to cascading attacks.

problem Cascading attacks on power grids and their undetected nature.
method Constrained Bayesian Optimization applied to transmission line protection settings.
result Even limited misconfiguration of protection settings can cause severe cascading attacks.

New insights into cascade feedback linearization of control systems.

problem Obtaining a cascade feedback linearization for invariant control systems.
method Introducing truncated versions of operators from the calculus of variations to prove new theorems.
result Established new geometry and foundational theorems for future work.

The paper improves theoretical guarantees for Thompson Sampling in cascading bandits.

problem Optimizing online recommender systems with cascading bandits.
method Develops and analyzes new Thompson Sampling algorithms for cascading bandits.
result Establishes the first theoretical guarantees on Thompson Sampling for cascading bandits.

Modeling cascading behavior in complex systems using CTBNs.

problem Understanding which states trigger cascading events in complex systems.
method Continuous-time Bayesian networks (CTBNs) for modeling and identifying likely sentry states.
result Identification of likely sentry states that may lead to cascading behavior.

Study on information cascade fragility under mismatched revealing probabilities.

problem Analyzing the fragility of information cascades in decision-making processes with imperfect knowledge of revealing probabilities.
method Examined sequential decision-making models with players having private information and imitating previous decisions. Studied the effect of a mismatch between players' beliefs and actual revealing probabilities.
result Derived closed-form expressions for optimal learning rates and identified phase transitions in the behavior of asymptotic learning rates.

A search engine usually outputs a list of KK web pages. The user examines this list, from the first web page to the last, and chooses the first attractive page. This model of user behavior is known as the cascade model. In this paper, we propose cascading bandits, a learning variant of the cascade model where the obje…

2015-02-10abs ↗pdf ↗

Bayesian optimization tackles expensive cascade processes.

problem Optimizing multistage decision-making processes with expensive costs.
method Formulated as Bayesian optimization framework with two types of acquisition functions.
result Demonstrated effectiveness through numerical experiments and a solar cell simulator application.

Model shows financial turbulence similar to turbulence, with wealth cascading from large to small entities.

problem Understanding wealth distribution and dynamics in financial systems.
method Constructed a multiscale model for hierarchical financial structures.
result Found wealth distribution exhibits power law at large scales and Maxwellian at small scales.

Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Faceb…

2016-02-02abs ↗pdf ↗

Modeling social network activity through user and topic interaction.

problem Understanding complex dynamics of opinion formation in social networks.
method Mixture of Interacting Cascades (MIC) model of marked multidimensional Hawkes processes.
result MIC achieves superior performance in modeling information cascade spread.

The paper addresses the kk-tangle enumeration problem. We introduce a notion of cascade diagram for kk-tangle projections. An effective enumeration algorithm for projections is proposed based on cascade representation. Tangles projections with up to 12 crossings are tabulated. We provide also pictures of alternating …

2007-12-22abs ↗pdf ↗

Unified approach for influence maximization using diffusion cascade representations.

problem Influence maximization on networks with diffusion cascades.
method Multi-task neural network learning influencer and susceptible vectors; greedy algorithm for influence maximization.
result IMINFECTOR outperforms other methods in efficiency and seed set quality.

Improves information cascade models using contrastive training and DSTs.

problem Improving models of information cascades using limited labeled data.
method Proposes a contrastive training procedure for models of information cascades as directed spanning trees (DSTs).
result Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.

A model infers dynamic networks from partial observation of cascading processes.

problem Inferring evolving networks from partial observation of node and edge data.
method A novel framework based on a mixture of coupled hierarchical Dirichlet processes.
result Explicit predictive distribution over edges of the underlying network, including future edges.

Cascading flows improve variational inference in structured programs.

problem Challenges in variational inference for complex probabilistic programs.
method Integrates normalizing flows and ASVI to create cascading flows, which embed the forward-pass of probabilistic programs.
result Cascading flows outperform normalizing flows and ASVI in structured inference problems.