Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise …
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48 results for “pairwise relationships”
Proposes a new method to learn distance metrics for semi-supervised learning.
problem Inconsistency between perturbed input sets and lack of pairwise relationship information.
method Metric Learning by Similarity Network (MLSN) co-training with a classification network to learn distance metrics adaptively.
result Performs better than state-of-the-art methods on empirical tasks.
Enhances GNNs by capturing node relationships, outperforming 2-WL test.
problem Inability of conventional GNNs to fully capture node relationships due to permutation invariance.
method Develops permutation-sensitive aggregation mechanism using permutation groups.
result Proves superior expressivity compared to 2-WL test and not less than 3-WL test.
This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of objects can be identified by standard sorting methods using pairwise comparisons. We are interested in natural situations in which relationships among the o…
Proposes a novel tensor-based approach for multi-level link prediction.
problem Inferring potential links from observed networks.
method Tensor-based joint network embedding capturing pairwise and hyperlinks.
result Improves hyperlink and pairwise link prediction accuracy.
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
problem Improving GNNs' ability to model feature interactions effectively.
method Investigates feature graphs and their importance in GNNs, using experiments and theoretical support.
result Edges between interacting features are crucial for GNNs, while non-interaction edges can degrade performance.
Cross-entropy loss linked to metric learning, outperforming complex pairwise losses.
problem Improving metric learning performance without complex optimization schemes.
method Theoretical analysis linking cross-entropy to pairwise losses, showing cross-entropy as an upper bound and equivalent to mutual information maximization.
result Minimizing cross-entropy is equivalent to maximizing mutual information, leading to state-of-the-art performance.
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense. Constrained clustering, as a subclass of SSL, makes use of user input in the form of relationships between data …
Optimized parallel algorithms for identifying strong ties in data.
problem Identifying strong ties in data with varying distances and community sizes.
method Design and analysis of sequential and parallel algorithms for partitioned local depths.
result Optimized algorithms achieve up to 19.4x speedup in parallel execution.
This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.
problem Identifying optimal arm pairs with varying costs in multi-armed bandits.
method Introduces a framework for pairwise pure exploration with arm-specific costs, derives a lower bound, and proposes CAET algorithm.
result CAET optimizes cumulative cost and approaches the lower bound asymptotically.
This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The 'cdt' package implements the end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the ca…
A fast algorithm speeds up training of pairwise kernels.
problem Training pairwise kernels efficiently for large datasets.
method Generalized vec trick for Kronecker product kernels.
result Pairwise kernels can be expressed as sums of Kronecker products.
Novel ML model predicts solvation free energies from atom interactions.
problem Predicting solvation free energies from atomistic interactions.
method Two encoding functions extract atomic feature vectors, interactions calculated by inner product.
result Outstanding performance and transferability on 6,493 experimental measurements.
We present a global optimization algorithm for clustering data given the ratio of likelihoods that each pair of data points is in the same cluster or in different clusters. To define a clustering solution in terms of pairwise relationships, a necessary and sufficient condition is that belonging to the same cluster sati…
ANN clusters multi-view data by agglomerating subviews and avoiding postprocessing.
problem Challenges in multi-view clustering, especially subview agglomeration.
method Agglomerative Neural Network (ANN) using Constrained Laplacian Rank.
result ANN outperforms state-of-the-art methods in multi-view clustering.
Statistical framework improves LLM chatbot ranking.
problem Improving evaluation of LLM-based chatbots through pairwise comparisons.
method Factored tie model, covariance modeling, and parameter constraints.
result Substantial improvements in modeling pairwise comparison data.
Structure of international trade hypergraphsphysics.soc-ph
Study the structure of international trade through hypergraphs.
problem Understanding the complex relationships in international trade networks.
method Analyzing the mean hyperdegree of adjacent vertices and decomposing correlation properties.
result Identifies bias in trade relationships not visible in pairwise networks.
Binary Hashing is widely used for effective approximate nearest neighbors search. Even though various binary hashing methods have been proposed, very few methods are feasible for extremely high-dimensional features often used in visual tasks today. We propose a novel highly sparse linear hashing method based on pairwis…
New method uses information theory to uncover causal relationships in complex systems.
problem Discovering causal relationships in multivariate systems, especially in Bayesian networks and hypergraphs.
method Partial Information Decomposition (PID) to explicitly model higher-order interactions.
result PID components reveal direct causal neighbors and collider relationships in Bayesian networks and multi-tail hyperedges in causal hypergraphs.
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obvious existence of such complex relationships in many real-world networ…
Feature selection has been proven a powerful preprocessing step for high-dimensional data analysis. However, most state-of-the-art methods tend to overlook the structural correlation information between pairwise samples, which may encapsulate useful information for refining the performance of feature selection. Moreove…
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to bea…
Method detects lead-lag relationships in multivariate time series.
problem Discovering lead-lag relationships in multivariate time series.
method Clustering-driven methodology using sliding window and various clustering techniques.
result Robust lead-lag estimates across clusters enhance consistent relationships identification.
Mixes higher-order simplicial complexes for data augmentation.
problem Lack of labeled data for complex systems with multiway interactions.
method Proposes mixup mechanisms for simplicial complexes, including linear and nonlinear mixup, and a convex clustering mixup.
result Synthetic simplicial complexes interpolate between existing data based on homomorphism densities.
The paper addresses privacy in rank aggregation using randomized responses.
problem Preserving privacy while aggregating pairwise rankings.
method Adaptive debiasing method for randomized response rankings.
result Established minimax rates for estimation errors and optimal privacy guarantees.
Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information los…
Unified framework for SSL methods linking contrastive and non-contrastive approaches.
problem Lack of theoretical foundations and design guidelines for SSL methods.
method Spectral manifold learning framework to unify SSL methods.
result Theoretical bridge between contrastive and non-contrastive methods.
Paper introduces MSA for weakly supervised covariance alignment in MEG signals.
problem Limited labeled signals in target datasets for MEG applications.
method Mixing model Stiefel Adaptation (MSA) leveraging unlabeled data.
result MSA outperforms recent methods in brain-age regression with MEG signals.
Detects lead-lag clusters in US equity market time series.
problem Identifying lead-lag relationships in multivariate time series.
method Directed network clustering of lead-lag relationships.
result Validated on US equity market data, detects statistically significant lead-lag clusters.
Estimating global pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We propose a non-parametric probabilistic method for detecting interaction effects of…
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connecte…
This paper presents a neural network-based end-to-end clustering framework. We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering. The network is trained with weak labels, specific…
Granger causality reviewed and advanced for complex data.
problem Validity of inferring causal relationships from time series data.
method Recent advances in models for high-dimensional time series, accounting for nonlinear and non-Gaussian observations, and sub-sampled data.
result Improved computational tools for Granger causality.
Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have a…
Simplicial neural networks extend graph neural networks to handle higher-order interactions.
problem Handling higher-order interactions in complex data structures.
method Define a convolution operation for simplicial complexes and use it to construct convolutional neural networks.
result SNNs effectively impute missing data in coauthorship complexes.
It is a longstanding problem to determine the precise relationship between the geodesic length spectrum of a hyperbolic manifold and its commensurability class. A well known result of Reid, for instance, shows that the geodesic length spectrum of an arithmetic hyperbolic surface determines the surface's commensurabilit…
Tree-Query uses LLMs to discover causal relationships in a transparent, interpretable manner.
problem Error propagation in classical causal discovery methods and opaque, confidence-free behavior of recent LLM-based causal oracles.
method Tree-Query is a tree-structured, multi-expert LLM framework that reduces causal discovery to queries about backdoor paths and dependencies.
result Tree-Query provides interpretable judgments with robustness-aware confidence scores and improves structural metrics over LLM baselines.
We study the problem of ranking from crowdsourced pairwise comparisons. Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous models for aggregation of pairwise comparisons do not focus on modeling such kind of biases. We introduce a new aggregation model factorBT …
Study tackles ranking fraud in online platforms by learning robust rankings.
problem Fraudulent fake users manipulate product rankings.
method Developed algorithms for robust ranking in two informational environments.
result Our algorithms converge to optimal rankings, robust to fake users.
Paper shows similarity learning can lead to strong binary classification performance.
problem How similarity learning can lead to good classification performance.
method Product-type formulation of similarity learning is connected to binary classification through an excess risk bound.
result Similarity learning can directly elicit a decision boundary for binary classification.
Paper learns hypergraph structures from signals with smoothness priors.
problem Learning hypergraph structures from signals with high-order relationships.
method Proposes HGSL framework with dual smoothness prior to map signals to hypergraph structure.
result HGSL efficiently infers meaningful hypergraph topologies from signals.
grangersearch: An R Package for Exhaustive Granger Causality Testing with Tidyverse Integrationstat.CO
grangersearch tests causal relationships in time series data.
problem Testing causal relationships between multiple time series.
method Exhaustive pairwise search, automatic lag order optimization, tidyverse integration.
result Automated Granger causality testing simplifies causal analysis.
Hierarchical clustering uses OWA operators to generalize linkage methods and avoid dendrogram inversions.
problem Avoiding unaesthetic inversions in hierarchical clustering dendrograms.
method OWA-based linkages combined with the Lance-Williams formula and conditions on weight generators.
result Conditions for weight generators to produce dendrograms without inversions.
An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can re…
Directional and pairwise measurements are often used to model inter-relationships in a social network setting. The Mixed-Membership Stochastic Blockmodel (MMSB) was a seminal work in this area, and many of its capabilities were extended since then. In this paper, we propose the \emph{Dynamic Infinite Mixed-Membership s…
In this study, a pairwise comparison matrix is generalized to the case when coefficients create Lie group , non necessarily abelian. A necessary and sufficient criterion for pairwise comparisons matrices to be consistent is provided. Basic criteria for finding a nearest consistent pairwise comparisons matrix (extend…
Paper introduces differential pairwise privacy for secure metric learning.
problem Securely measuring similarities of individuals given sensitive pairwise data.
method Develops differential pairwise privacy (DPP) to protect sensitive pairwise data.
result Achieves pairwise data privacy without significant performance loss.
Develops a statistical framework to measure uncertainty in model rankings based on human preferences.
problem Uncertainty in model rankings based on human preferences due to mismatch between human and model preferences.
method Statistical framework using pairwise comparisons by humans and models to provide rank-sets for each model.
result Rank-sets constructed using only pairwise comparisons by strong models often do not cover the true ranking of human preferences.