We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence maximization. Motivated by the recent criticism on the effectiveness o…
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48 results for “influence diffusion”
New method reveals how training data influence diffusion model outputs.
problem Difficulty in assessing training data impact on diffusion model outputs.
method Use of ensembles trained on carefully engineered splits of training data to identify influential training examples.
result Demonstrated the viability of ensembles as generative models and validity of assessing influence.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
Efficiently selects seed nodes to maximize content influence in unknown social networks.
problem Maximizing content spread in social networks with unknown network model.
method Formulated as an infinite-horizon discounted MDP, uses model-based reinforcement learning to select seed users adaptively.
result Established a regret bound of for the algorithm.
DataInf efficiently approximates data influence in large models, improving transparency and identifying mislabeled data.
problem Efficiently estimating data influence in large-scale models like LoRA-tuned LLMs and diffusion models.
method DataInf uses a closed-form expression to approximate influence scores efficiently.
result DataInf outperforms existing methods in computational and memory efficiency, accurately identifying influential data points.
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…
Nonparametric method measures influence of training images on diffusion model outputs.
problem Quantifying influence of individual training examples on diffusion model outputs.
method Patch-level similarity between generated and training images, using optimal score function.
result Strong attribution performance, matching gradient-based approaches and outperforming baselines.
Influence maximization (IM) is the problem of finding for a given a set of nodes in a network with maximum influence. With stochastic diffusion models, the influence of a set of seed nodes is defined as the expectation of its reachability over simulations, where each simulation specifies a det…
A new neural network approach for diffusion on networks.
problem Inference and estimation of diffusion on network structures.
method Neural mean-field dynamics derived from Mori-Zwanzig formalism, approximated by learnable time convolution operators.
result Significantly outperforms existing approaches in accuracy and efficiency.
Investor-driven information diffusion affects excess comovement in China and the U.S. markets.
problem Investor-driven information diffusion and its impact on excess comovement.
method Cross-sectional analysis of 4,533 Chinese and 4,517 U.S. stocks from 2010 to 2022.
result Retail-driven information diffusion significantly drives excess comovement in China, while institution-driven diffusion is the primary driver in the U.S.
Sparsity helps reduce diffusion model costs.
problem High computational costs in diffusion models.
method Introduced sparsity concept to reduce input dimensionality.
result Sparsity reduces computational complexity to intrinsic data dimension.
New framework for online influencer selection considering cost constraints.
problem Real-world social advertising budget limitations and cost variability of influencers.
method Introduces a budgeted framework for online influence maximization using an algorithm with semi-bandit feedback.
result Improves the state of the art regret bound for cardinality constraint setting.
Recovering hidden influence networks from cascade data using Jacobian-based machine learning.
problem Recovering influence networks behind dynamic cascades.
method CascadeNet, a Jacobian-based machine learning framework.
result CascadeNet achieves high accuracy in network recovery.
In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …
Replacing MSE with f-divergence in diffusion models improves robustness under data contamination.
problem Improving robustness of diffusion models under data contamination.
method Replacing MSE with f-divergence in diffusion models.
result Empirical improvement in performance under data contamination.
Many works have been proposed in the literature to capture the dynamics of diffusion in networks. While some of them define graphical markovian models to extract temporal relationships between node infections in networks, others consider diffusion episodes as sequences of infections via recurrent neural models. In this…
Different technological domains have significantly different rates of performance improvement. Prior theory indicates that such differing rates should influence the relative speed of diffusion of the products embodying the different technologies since improvement in performance during the diffusion process increases th…
New algorithm for competing influence spread in unknown networks.
problem Maximizing influence spread in a social network with unknown probabilities.
method Combinatorial multi-armed bandit (CMAB) framework, Triggering Probability Modulated (TPM) condition, OCIM-TS, OCIM-OFU, OCIM-ETC.
result Sublinear Bayesian and frequentist regret for OCIM-TS and OCIM-OFU, respectively.
The Thermodynamic Approach to Whole-Life Insurance: A Method for Evaluation of Surrender Riskq-fin.GN
Model evaluates insurance risk using thermodynamic principles.
problem Risk of lapses due to adverse selection in insurance.
method Collective model with diffusion process influenced by statistical mechanics.
result Derives level premium to evaluate insurance risk.
A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited bu…
We analyze geometrical structures necessary to represent bulk and surface interactions of standard and substructural nature in complex bodies. Our attention is mainly focused on the influence of diffuse interfaces on sharp discontinuity surfaces. In analyzing this phenomenon, we prove the covariance of surface balances…
This research compares masked diffusion models to autoregressive language models, focusing on architectural differences.
problem Comparing masked diffusion models to autoregressive language models due to architectural differences.
method Equitably compare MDMs within a decoder-only framework, investigating architectural influences.
result Decoder-only MDMs can achieve significant speedups and comparable perplexity with temperature annealing.
Researchers improve imputation of missing data using diffusion transformers, quantifying uncertainty.
problem Enhancing the quality of time-series data with missing values.
method Conditional diffusion transformers for imputation, with statistical sample complexity bounds and uncertainty quantification.
result Theoretical insights into the efficiency and accuracy of imputation, influenced by missing patterns.
Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed {\em deterministic} optimization problems. These methods employ constant step-sizes and converge linearly to the {\em exact} solution under proper conditions. However, their per…
The paper analyzes how guidance affects diffusion models using Gaussian mixture models.
problem Understanding how guidance influences diffusion models in specific contexts.
method Theoretical study using Gaussian mixture models and comparison inequalities for differential equations.
result Guidance boosts classification confidence but reduces distribution diversity, leading to lower differential entropy.
Deterministic and stochastic influences on Japan and US stock and foreign exchange markets. A Fokker-Planck approachcond-mat.stat-mech
The evolution of the probability distributions of Japan and US major market indices, NIKKEI 225 and NASDAQ composite index, and and currency exchange rates is described by means of the Fokker-Planck equation (FPE). In order to distinguish and quantify the deterministic and random influences on these…
Improved sampling in generative models using CLDs with a hyperparameter.
problem Improving sampling performance in generative models.
method Extending Critically-damped Langevin Diffusions with a hyperparameter to control noise.
result Derivation of a novel upper bound on Wasserstein sampling error.
The rise of Online Social Networks (OSNs) has caused an insurmountable amount of interest from advertisers and researchers seeking to monopolize on its features. Researchers aim to develop strategies for determining how information is propagated among users within an OSN that is captured by diffusion or influence model…
New algorithm estimates influence in social networks without sampling.
problem Estimating the number of influenced nodes in social networks.
method Dynamic message-passing approach for scalable influence estimation.
result Upper bound on expected spread for general graphs, exact for treelike networks.
This research analyzes and accelerates score-based diffusion models using discretization and Hessian information.
problem Theoretical foundations and convergence analysis of score-based diffusion models.
method Investigation of various discretization schemes, including Euler, exponential integrators, and midpoint randomization. Proposal of an accelerated sampler based on local linearization method.
result Hessian-based approach achieves faster convergence rates of order $\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon}
ight)$, significantly improving upon vanilla diffusion models.
ABC method analyzes diffusion model dependence without retraining.
problem Difficulties in characterizing diffusion models' dependence on training data.
method Ablation-based counterfactuals using model components trained on different splits.
result Demonstrates limits of training data attribution and existence of unattributable samples.
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passescs.LG
New method estimates data influence efficiently by leveraging test samples.
problem Efficiently estimating influence of training data on model predictions.
method Mirrored Influence Hypothesis, forward pass for test samples.
result Significant improvement in efficiency over existing methods.
The study uncovers the conditions under which diffusion models memorize or generalize.
problem Understanding the balance between memorization and generalization in diffusion models.
method Theoretical and mathematical framework to investigate memorization and generalization in diffusion models.
result Theoretical crossover point predicts a phase transition in diffusion models, validating the hypothesis.
Early stopping improves sample quality in latent diffusion models.
problem Latent diffusion models degrade sample quality with conventional early stopping.
method Analyzed the interaction between latent dimension and stopping time under Gaussian framework.
result Lower-dimensional representations benefit from earlier termination, higher-dimensional spaces require later stopping.
Generative models use latent abstractions to create images.
problem Understanding how generative models create high-dimensional data like images.
method Developed a theoretical framework using SDE and information theory.
result Diffusion models can be seen as a non-linear filter driven by latent abstractions.
FININ predicts financial markets by modeling news interactions and influence.
problem Complex diffusion of financial news into market prices.
method FININ is a novel model that captures news links and interactions, integrating market data and news articles.
result FININ outperforms advanced models with a 0.429 and 0.341 improvement in daily Sharpe ratio for S&P 500 and NASDAQ 100 respectively.
New model scales MHPs for analyzing large-scale diffusion processes.
problem Complex temporal dependencies in MHPs make them hard to scale.
method Exploits sparsity in diffusion processes to compute MHP likelihood and gradients efficiently.
result Improves runtime performance by multiple orders of magnitude on sparse event sequences.
Asymptotic Separability of Diffusion and Jump Components in High-Frequency CIR and CKLS Modelsq-fin.ST
Robustly detects jumps in high-frequency CIR and CKLS models.
problem Jump detection in high-frequency jump-diffusion processes.
method MDPDE-based robust estimators for drift and diffusion coefficients.
result Maximum of normalized residuals converges to Gumbel distribution.
New method predicts model output distributions to improve data attribution.
problem Traditional training data attribution ignores randomness in model training.
method Distributional Training Data Attribution (d-TDA) using influence functions.
result Influence functions are effective and emerge naturally from d-TDA.
Modeling tech transfer to explain convergence in Central and Eastern Europe.
problem Understanding mechanisms of technological diffusion in developing economies.
method Introducing a herding-based mechanism to model technological adoption and productivity growth.
result Explicit analytical solution showing nonlinear convergence to a moving frontier.
A novel diffusion method for Bayesian posterior sampling with theoretical guarantees.
problem Efficiently sampling from complex posterior distributions in Bayesian inversion.
method Diffusion-based posterior sampling using Langevin dynamics and PnP framework.
result The method converges even for multi-modal posterior distributions with theoretical error bounds.
The study examines how the number of noise samples affects diffusion models' performance.
problem Understanding the balance between generalization and memorization in diffusion models.
method Theoretical analysis and empirical experiments with Denoising Score Matching (DSM) using random features.
result Precise expressions for test and train errors under specific conditions reveal the mechanisms of generalization and memorization.
New study on guidance in masked diffusion models, showing how it shapes sampling dynamics.
problem Understanding how guidance influences the sampling behavior of masked diffusion models.
method Derived explicit solution to guided reverse dynamics, analyzing effects in 1D and 2D.
result Guidance amplifies class-specific regions and suppresses shared regions, affecting covariance structures.
In order to investigate the origin of large price fluctuations, we analyze stock price changes of ten frequently traded NASDAQ stocks in the year 2002. Though the influence of the trading frequency on the aggregate return in a certain time interval is important, it cannot alone explain the heavy tailed distribution of …
A (reactive) lattice-gas approach to economic cyclescond-mat.stat-mech
A microscopic approach to macroeconomic features is intended. A model for macroeconomic behavior under heterogeneous spatial economic conditions is reviewed. A birth-death lattice gas model taking into account the influence of an economic environment on the fitness and concentration evolution of economic entities is nu…
Influential node detection is a central research topic in social network analysis. Many existing methods rely on the assumption that the network structure is completely known \textit{a priori}. However, in many applications, network structure is unavailable to explain the underlying information diffusion phenomenon. To…
Study uses neural networks to value Bitcoin options considering price jumps and sentiment.
problem Valuing Bitcoin options under price jumps and market sentiment.
method Bivariate jump-diffusion model, incorporating Google search sentiment, and artificial neural networks.
result Derives a closed formula for Bitcoin option pricing and validates using high-volatile stocks.