This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
P&C combines multiple perturbed graphs to improve influential spreader detection.
problem Ineffective algorithms are unstable to small network perturbations.
method Creates multiple perturbed graphs, applies scoring function to each, and combines results.
result P&C significantly improves influential spreader detection without extra cost.
Hidden Markov Model helps track asymptomatic carriers in pandemic.
problem Tracking spread of asymptomatic carriers (super-spreaders) during pandemic.
method Applied Hidden Markov Model to analyze COVID-19 data.
result Better assessment of spread extent for calibrated interventions.
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.
Algorithm detects influential observations in high-dimensional data.
problem Challenges in identifying influential observations in high-dimensional datasets.
method Three-step algorithm based on expectiles and asymmetric correlations.
result Higher detection power than competing methods.
Identifies influential users in collaborative filtering systems.
problem Impact of influential users on collaborative filtering systems.
method Formalized influence, identified influential users, analyzed impact across domains.
result Identified and characterized influential users and analyzed their impact.
The study identifies influential factors in VAE representations.
problem Identifying and supervising influential factors in VAE representations without manual intervention.
method Mutual information as a necessary indicator to discover influential factors; design an algorithm for calculating mutual information.
result Mutual information influences the lower bound of VAE's reconstruction error and downstream classification task.
Develops a framework to test excessive influence of small data subsets.
problem Identifying when small data subsets significantly impact model conclusions.
method Formalizes the concept of most influential sets, deriving influence formulas and extreme value distributions.
result Allows rigorous hypothesis testing for excessive influence, resolving contested findings.
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…
Proposes an algorithm for data cleansing without domain knowledge.
problem Improving machine learning model accuracy without extensive domain knowledge.
method Infers influential instances by retracing SGD steps and incorporating intermediate models.
result Accurately infers influential instances for models trained with SGD.
A method for finding most influential sets reduces a complex problem to a sequence of simpler top-k problems.
problem Identifying most influential subsets in complex models.
method Reduces the problem to a sequence of top-k problems using Dinkelbach's method. result The method returns a globally optimal set for the univariate ratio objective, including partial linear models.
Paper ranks influential Tor Darknet onion domains using content features.
problem Measuring influence of criminal onion domains in Tor Darknet.
method Content-based features from multiple sources, Learning-to-Rank approach.
result Listwise approach outperforms other methods with NDCG of 0.95 for top-10.
We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model's predictions change upon leave-one-out retraining, leaving out each…
Proposes second-order influence functions for identifying influential groups in test-time predictions.
problem Identifying influential groups in test-time predictions for black-box models.
method Second-order approximations of the effect of removing a group of training samples on model predictions.
result Improves the correlation between computed influence values and ground truth values for linear models.
Improves many-shot learning by optimizing and generating influential examples.
problem Limited performance in many-shot in-context learning (ICL).
method Iterative optimization and generation of influential examples.
result Significant improvements across various tasks using BRIDGE.
A model predicts influential nodes in complex networks by considering indirect interactions.
problem Identifying influential nodes in complex networks using indirect interactions.
method Proposes MOGen, a multi-order generative model that considers all indirect influences up to a maximum distance.
result MOGen consistently outperforms network models and path-based approaches in predicting influential nodes.
Improved real-time visualizations of conversation turns using dynamic attention weights.
problem Uniform attention weights in sequential analysis tasks prevent meaningful visualization.
method Developed a method to track changes in turn importance over time.
result More informative real-time visuals confirmed by human reviewers.
There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial attack. In order to solve the "needle in the haystack" problem of which samples t…
It is illustrated a methodology to compute the pure premium for the automobile insurance (claim frequency and severity) using generalized linear models. It is obtained the pure premium for the partial damage loss cover (PPD) using a set of automobile insurance policies with an exposition of a year. It is found that the…
Study shows influence functions are poor for neural networks but useful for identifying influential examples.
problem Influence functions misalign with leave-one-out retraining in neural networks.
method Decomposed the discrepancy into five terms and studied their contributions across different architectures and datasets.
result Influence functions are a good approximation to the proximal Bregman response function (PBRF), useful for identifying influential examples.
Framework detects influential actors in disinformation networks.
problem Identifying and countering hostile influence operations on social media.
method Combines NLP, ML, graph analytics, and causal inference.
result 96% precision, 79% recall, 96% PR-curve area for IO detection.
MISS identifies influential subsets in ML models.
problem Capturing collective influence of training samples.
method Analyzed and compared influence-based greedy heuristics and adaptive versions.
result Adaptive heuristics can better capture sample interactions.
For any matrix A in R^(m x n) of rank ρ, we present a probability distribution over the entries of A (the element-wise leverage scores of equation (2)) that reveals the most influential entries in the matrix. From a theoretical perspective, we prove that sampling at most s = O ((m + n) ρ^2 ln (m + n)) entries of the ma…
New method learns influential action sequences without privileged final states.
problem Learning meaningful action sequences in large action spaces.
method Model-free approach that considers the full trajectory.
result Successfully applied to large action spaces.
A game-theoretic framework identifies influential hyperparameters for neural networks.
problem Understanding which hyperparameters are most important for neural network performance.
method Employing Shapley Effects for global sensitivity analysis and Pareto front sets for identifying effective configurations.
result Reveals which hyperparameters are most influential for different objectives in neural networks.
Proposes a new sensitivity measure for optimization problems.
problem Optimization of high-dimensional functions with expensive computer codes.
method Introduces a new influence measure based on the Hilbert-Schmidt Independence Criterion.
result The new measure significantly reduces the number of function evaluations.
Kurdistan Region is a tourist hub. This research analyzes other Non-Oil Sectors that have huge attractions of Foreign Direct Investments into the Kurdistan Region from 2005 to 2013. Comparative analysis was carried out between Iraq and the Region, and among influential Sectors of the Economy. T-test and ANOVA are stati…
CorePPR combines PageRank and CoreRank for scalable GNNs.
problem Expensive message passing in GNNs for large graphs.
method Learnable convex combination of approximate PPR and CoreRank, dynamic node selection.
result CorePPR outperforms PPRGo on large graphs.
As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model w…
Study uses VC correlation to uncover directional financial relationships.
problem Understanding causal relationships between financial variables.
method Volatility constrained correlation (VC correlation) method.
result Operating income is most influential, while market capitalization and revenue are most susceptible.
Bayesian approach scores influential training examples for model predictions.
problem Enhance interpretability and safety of machine learning models.
method Formulate TDA as a Bayesian information-theoretic problem, scoring subsets by information loss.
result Method aligns with classical influence scores while promoting diversity for subsets.
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
We prove that the moduli space of 2-convex embedded n-spheres in R^{n+1} is path-connected for every n. Our proof uses mean curvature flow with surgery and can be seen as an extrinsic analog to Marques' influential proof of the path-connectedness of the moduli space of positive scalar curvature metics on three-manifold…
In terms of transfer entropy, we investigated the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analy…
Discusses the tight versus overtwisted dichotomy in 3D contact geometry.
problem Understanding the tight versus overtwisted dichotomy in 3D contact geometry.
method Reviews Eliashberg's seminal work and contributions to the theory.
result Explains the genesis and importance of the tight versus overtwisted dichotomy.
Two novel methods identify influential features in CMABs for better reward distribution.
problem Suboptimal features degrade rewards, interpretability, and efficiency in CMABs.
method Heterogeneous Incremental Effect (HIE) and Heterogeneous Distribution Divergence (HDD) methods.
result Consistent ability to identify influential HTE features, enhancing CMAB performance.
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions. However, recent findings have highlighted the fragili…
Paper develops a method to highlight influential transitions for off-policy evaluation.
problem Validating off-policy evaluation in high-stakes settings.
method Highlight influential transitions, develop influence functions for Q-evaluation.
result Method can identify limitations and make evaluation more robust.
Paper proposes adversarial modifications for link prediction models to improve robustness and interpretability.
problem Improving accuracy is not enough; robustness and interpretability are also crucial for link prediction models.
method Adversarial modifications to identify influential facts and evaluate model sensitivity and interpretability.
result The approach identifies the most influential facts and evaluates the sensitivity of link prediction models to additional facts.
MaGNet integrates local and global graph information for interpretable results.
problem Lack of interpretability and inability to learn varying-order representations in GNNs.
method Model-agnostic Graph Neural Network (MaGNet) framework integrating estimation and interpretation models.
result MaGNet effectively integrates information of various orders and provides meaningful, interpretable results.
Aiming at quantifying and evaluating the regional commercial environment along with the level of economic development among cities in mainland China, the concept of China City Commercial Environment Credit Index(CEI) was first introduced and established in 2010. In this manuscript, a historical review and detailed intr…
In a very influential paper Gehring and Palka introduced the notions of quasiconformally homogeneous and uniformly quasiconformally homogeneous subsets of Euclidean space. Their motivation was to provide a characterization of quasi-disks, i.e. domains which are quasiconformally homeomorphic to the unit disk. As a gener…
Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagn…
We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs. We present a new Bayesian global optimization algorithm, called Stratified Bayesian Optimization (SBO), which uses this strong depen…
Data poisoning attacks can manipulate recommender systems to recommend target items.
problem Attacks on recommender systems to influence top-N item recommendations.
method Formulated as an optimization problem, solved using influence function to select influential users.
result Effective data poisoning attacks that outperform existing methods.
Establishes statistical and computational bounds for influence diagnostics.
problem Identifying influential datapoints or subsets in machine learning models.
method Finite-sample statistical bounds and computational complexity for influence functions and approximate maximum influence perturbations.
result Established statistical and computational guarantees for influence diagnostics.
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We p…
This work defines observation-specific explanations for black-box models.
problem Assigning importance to data points in black-box model predictions.
method Surrogate model construction using scattered data approximation and orthogonal matching pursuit.
result Validated approach on simulated and real-world datasets.