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…
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48 results for “cascading”
Method predicts diffusion reach probabilities using node embeddings.
problem Estimating diffusion reach probabilities with limited cascades and network information.
method Representation learning on node embeddings for cascade prediction.
result Proposed method outperforms using available cascade data.
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.
Model infers diffusion networks from heterogeneous cascade data.
problem Understanding and predicting diffusion processes in interconnected populations.
method Double mixture directed graph model with layer-specific constraints.
result Convex formulation allows for statistical and computational guarantees.
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.
Explicit size distributions of failure cascades redefine systemic risk on finite networksphysics.soc-ph
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.
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.
New approach to learning cascaded classifiers for heterogeneous embedded devices.
problem Real-time monitoring and adaptive interventions in mobile health.
method Generalized tree-structured cascades, joint learning of classifiers, novel output combination during training.
result Joint learning of all detectors for tree-structured cascades in resource-constrained environments.
Do Cascades Recur?cs.SI
The study finds that many social media content cascades recur with bursts of popularity over time.
problem Understanding the recurrence of content cascades on social media.
method Large-scale analysis of cascades on Facebook over extended time periods, using a simple model to simulate recurrence.
result Content virality drives recurrence, but beyond a certain popularity, the rate of recurrence decreases.
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 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…
Paper uses RL to mitigate cascading failures in power systems.
problem Mitigating multi-stage cascading failures in power grids.
method Reinforcement Learning applied to DC-OPF for power flow optimization.
result Reduced system collapse rates through RL-based optimization.
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…
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.
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 -tangle enumeration problem. We introduce a notion of cascade diagram for -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 …
Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we …
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…
DICE estimates data influence cascade in decentralized learning networks.
problem Lack of fair incentives discourages participation in decentralized learning.
method Designs DICE to estimate influence cascade in decentralized networks.
result Influence cascade is influenced by data, topology, and loss landscape curvature.
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.
Framework predicts contagion in assortative banking networks.
problem Predicting contagion in assortative banking networks.
method Probabilistic framework with explicit edge probabilities, analyzing default cascades.
result Cascade condition characterizes systemic risk in banking networks.
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 cascaded CNN reduces artifacts in low-dose CT image denoising.
problem Reduce artifacts in denoised low-dose CT images.
method Cascaded training of CNNs on a dataset to iteratively improve denoising performance.
result The cascaded CNN approach significantly reduces artifacts in denoised CT images.
Paper presents a probabilistic model to improve LLM cascade performance.
problem Complexity of LLM cascades and their interaction error rates.
method Probabilistic model for joint performance distribution of LLMs.
result Improves area under the error-cost curve by 4.3% on average for cascades with k≥3 models.
Model explains financial market intermittency with negative correlation.
problem Verifying multifractality in financial markets.
method Extended multiplicative random cascade model with an additional stochastic term.
result Model accurately reproduces empirical financial data.
Model financial default cascades on sparse graphs via hitting times.
problem Capturing systemic risk in large, sparsely-connected financial networks.
method Dynamic particle systems with hitting times and convergence theory.
result Characterization of default time distribution in tree-like networks.
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.
Develops a novel framework for activity shaping in social networks.
problem Controlling user activity spread in social networks.
method Optimizes the spectral radius of the Hazard matrix for local control actions.
result Minimizes the influence of information cascades through targeted resource allocation.
A real-time adaptive background subtraction framework using a rejection cascade of Gaussians.
problem Real-time background subtraction with pixel-wise modeling trade-offs.
method Decompose Gaussian Mixture Model into an adaptive cascade of Gaussians (CoG).
result 4-5x speed-up and 17% accuracy improvement over baseline.
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.
I show the equivalence between a model of financial contagion and the threshold model of global cascades proposed by Watts (2002). The model financial network comprises banks that hold risky external assets as well as interbank assets. It is shown that a simple threshold model can replicate the size and the frequency o…
Paper proposes real-time health monitoring using wearable sensors.
problem Real-time health monitoring using wearable sensors.
method Learning shallow detection cascades for real-time wearable-phone or wearable-phone-cloud systems.
result Cigarette smoking detection from actigraphy and respiration data.
A method to reduce computation by dynamically sacrificing accuracy in deep neural networks.
problem Balancing computational effort and classification accuracy in deep neural networks.
method A cascade of deep neural networks with dynamically set confidence thresholds based on softmax outputs.
result Reduces 15%-50% in MAC operations with a 1% accuracy degradation.
A new framework approximates covariance matrices using tree decompositions.
problem Approximating covariance matrices for Gaussian distributions.
method Cascade of tree decompositions with Cholesky factorization.
result The proposed framework guarantees convergence and outperforms KL divergence.
Study online ranking in non-stationary environments with abrupt preference changes.
problem Online learning to rank in a non-stationary cascade model where user preferences change abruptly.
method Cascading non-stationary bandits, proposing CascadeDUCB and CascadeSWUCB algorithms.
result Upper and lower bounds on regret for cascading non-stationary bandits algorithms.
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.
Paper develops a neural model to assess cascading extreme events.
problem Risk assessment of domino effects like earthquakes and tsunamis.
method Develops a Kolmogorov-Arnold neural network (KANE) framework.
result Estimates the probability of one extreme event triggering another.
A new algorithm balances exploration and exploitation in online decision-making.
problem Balancing exploration and exploitation in online decision-making.
method Proposed -UCB algorithm incorporating conservative mechanism.
result Proved n-step upper regret bound for two situations.
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…
In the Network Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. In this paper, we approach this problem from the sparse recovery perspective. We introduce a general model of cascades, including the voter model and the independent cascad…
We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional particle filtering algorithms. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm i…
This work proposes an efficient autoregressive model for text generation.
problem The challenge of generating high-quality text with autoregressive models.
method Introduces a cascaded decoding approach using Markov transformers to achieve sub-linear parallel time generation.
result Shows competitive accuracy/speed tradeoff compared to existing methods on five machine translation datasets.
Adaptive cascade submodular maximization tackles sequential selection under uncertainty.
problem Maximizing expected utility from a set of items with unknown states and continuation probabilities.
method Proposed adaptive cascade submodular functions and a 0.12 approximation algorithm.
result Identified a class of functions (adaptive cascade submodular) that many practical applications satisfy.
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…
Strength of weak layers in cascading failures on multiplex networks: case of the international trade networkphysics.soc-ph
Model shows cascading failures are more severe in multiplex networks than single-layer networks.
problem Underestimation of risks in single-layer network analyses due to overlooked impact of weak layers.
method Simple model of cascading failure on multiplex networks of weight-heterogeneous layers.
result Multiplex model produces more catastrophic cascading failures than single-layer model.
New algorithm for online recommendation of top items from large lists.
problem Online recommendation of top items from a large set of candidates.
method Cascading bandits, linear generalization, learning attraction probabilities from features.
result Regret bound for one algorithm, superior performance on recommendation problems.