Relationships in online social networks often imply social connections in the real world. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of …
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48 results for “relationships”
Proposes a method to generate realistic counterfactuals by learning relationships.
problem Counterfactual explanations often ignore intrinsic relationships between data attributes.
method Uses a variational auto-encoder to learn relationships and perturb the latent space.
result The model preserves relationships and generates realistic counterfactuals.
Modeling lead-lag relationship between two text corpora for improved topic modeling.
problem Recognizing the relationship between multiple text corpora for better topic modeling.
method Proposed a jointly dynamic topic model and embedding extension for large-scale text corpus.
result The proposed model can well recognize the lead-lag relationship between two text corpora and improve topic learning.
New method evaluates financial graphs for stock trend forecasting.
problem Lack of dynamic stock relationship graphs and evaluation methods.
method SPNews dataset and novel evaluation methods independent of downstream tasks.
result Evaluation methods can differentiate between various financial relationship graphs.
Previous models for learning entity and relationship embeddings of knowledge graphs such as TransE, TransH, and TransR aim to explore new links based on learned representations. However, these models interpret relationships as simple translations on entity embeddings. In this paper, we try to learn more complex connect…
Relationship lending is broadly interpreted as a strong partnership between a lender and a borrower. Nevertheless, we still lack consensus regarding how to quantify the strength of a lending relationship, while simple statistics such as the frequency and volume of loans have been used as proxies in previous studies. He…
Shifu2 discovers advisor-advisee relationships in collaboration networks.
problem Discovering hidden advisor-advisee relationships in scientific collaboration networks.
method Network Representation Learning (NRL) model, considering both network structure and node/edge semantics.
result Improved stability and effectiveness compared to state-of-the-art methods.
Algorithm detects lead-lag relationships in multivariate time series.
problem Understanding temporal dependencies between time series.
method Cluster-driven methodology based on dynamic time warping.
result Robust detection of lead-lag relationships in lagged multi-factor models.
Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals. We define the notion of a sub-interval relationship (SIR) to capture such interactions that are prominent only …
Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated wi…
Study on the relationship between explanations and predictions in machine learning models.
problem Understanding the relationship between explanations and predictions in machine learning models.
method Causal inference to measure treatment effect on hyperparameters and inputs.
result The relationship between explanations and predictions is far from ideal, especially in high-performing models.
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
For analysis of a high-dimensional dataset, a common approach is to test a null hypothesis of statistical independence on all variable pairs using a non-parametric measure of dependence. However, because this approach attempts to identify any non-trivial relationship no matter how weak, it often identifies too many rel…
This paper proposes a method to reveal task relationships in multi-task learning models using sparse graphs.
problem Understanding the underlying task relationships in multi-task learning models.
method Proposes a bilevel formulation of multi-task learning that induces sparse graphs.
result The method improves interpretability of multi-task learning models without sacrificing generalization performance.
Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processi…
Proposes C2RM to mine cross-cryptocurrency relationships for better Bitcoin price prediction.
problem Limited consideration of historical relationships and interactions between cryptocurrencies for Bitcoin price prediction.
method C2RM module using Dynamic Time Warping for lead-lag relationship extraction and aggregation.
result Improves existing price prediction methods by significant performance improvement.
New method uses SEMs to uncover cause-effect in manufacturing processes.
problem Complex cause-and-effect relationships in manufacturing processes.
method Using Structural Equation Models with non-linear relationships.
result More informative cause-effect relationships derived from data.
SMART: A Flexible Approach to Regression using Spline-Based Multivariate Adaptive Regression Treesstat.ML
SMART combines decision trees and MARS for better regression modeling.
problem High variance in decision trees for continuous relationships, poor performance in MARS for discontinuities.
method SMART uses a decision tree to identify subsets with distinct continuous relationships, then applies MARS to fit these relationships independently.
result SMART improves regression performance over state-of-the-art methods in capturing discontinuities and continuous relationships.
This paper presents a novel multitask multiple kernel learning framework that efficiently learns the kernel weights leveraging the relationship across multiple tasks. The idea is to automatically infer this task relationship in the \textit{RKHS} space corresponding to the given base kernels. The problem is formulated a…
Double autoencoder improves missing value imputation in recommender systems.
problem Imputing missing values in tables using row-row and column-column relationships.
method Simultaneously uses row-row and column-column relationships through a double autoencoder.
result outperforms state-of-the-art models in recommender systems.
Paper uses HGNN to predict stock types from relationships and temporal data.
problem Predicting stock types from complex market data.
method Integrates stock relationships and temporal data using HGNN.
result Effective prediction of stock types with HGNN model.
The existence of time-lagged cross-correlations between the returns of a pair of assets, which is known as the lead-lag relationship, is a well-known stylized fact in financial econometrics. Recently some continuous-time models have been proposed to take account of the lead-lag relationship. Such a model does not follo…
New method uses entropy to generate multiple plausible causal maps.
problem Learning causal relationships from noisy data can lead to artifacts in DAGs.
method Entropy-based inference to generate an ensemble of plausible causal graphs.
result Multiple causal maps consistent with underlying data variability.
We prove an exact relationship between the optimal denoising function and the data distribution in the case of additive Gaussian noise, showing that denoising implicitly models the structure of data allowing it to be exploited in the unsupervised learning of representations. This result generalizes a known relationship…
Time-series data is being increasingly collected and stud- ied in several areas such as neuroscience, climate science, transportation, and social media. Discovery of complex patterns of relationships between individual time-series, using data-driven approaches can improve our understanding of real-world systems. While …
Rhino learns causal relationships from time series data with history-dependent noise.
problem Discovering causal relationships from time series data with non-linear relations, instantaneous effects, and history-dependent noise.
method Combines vector auto-regression, deep learning, and variational inference.
result Demonstrates better causal relationship discovery performance compared to baselines.
Multi-view data are increasingly prevalent in practice. It is often relevant to analyze the relationships between pairs of views by multi-view component analysis techniques such as Canonical Correlation Analysis (CCA). However, data may easily exhibit nonlinear relations, which CCA cannot reveal. We aim to investigate …
Bayesian model reduces stock volatility by identifying key cointegrated relationships.
problem Constructing low volatility stock portfolios from a large number of stocks.
method High dimensional Bayesian cointegration estimation.
result Portfolios with reduced volatility and persistence of cointegration relationships.
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals reco…
We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers…
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus…
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.
Proposes a partially linear structure to capture nonlinear relationships in mixture of experts models.
problem Suboptimal estimates due to linearity assumption in mixture of experts models.
method Introduces a partially linear structure that incorporates unspecified functions to capture nonlinear relationships.
result Establishes the identifiability of the proposed model under mild conditions and introduces a practical estimation algorithm.
Extended spinor connections associated with composite spin-tensorial bundles are considered. Commutation relationships for covariant and multivariate differentiations and corresponding curvature spin-tensors are derived.
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations …
Company Competition Graphq-fin.GN
System constructs public competitor graph from financial reports.
problem Time-consuming and expert-laden manual extraction of corporate relationships.
method Financial report processing to generate reliable knowledge graph of corporate relationships.
result More than 83% of S\&P 500 companies' competition relationships retrieved.
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decompositioncs.LG
Method discovers local independence in systems with continuous variables.
problem Applying Context-Specific Independence (CSI) to continuous variables is impractical.
method Neural contextual decomposition (NCD) learns partition of joint outcome space.
result NCD successfully discovers local independence in synthetic and real-world systems.
Long Term Economic Relationships From Cointegration Mapsphysics.soc-ph
We employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the Sorting Points Into Neighborhoods (SPIN) technique, which has been previously used to ana…
Socio-economic inequality is characterized from data using various indices. The Gini () index, giving the overall inequality is the most common one, while the recently introduced Kolkata () index gives a measure of fraction of population who possess top fraction of wealth in the society. Here, we show t…
A conjecturing machine uncovers feature relationships in data.
problem Discovering nonlinear and boolean relationships among features.
method Proposes a conjecturing machine that suggests feature relationships using bounds and expressions.
result Reveals true underlying relationships in datasets.
Capsule networks excel in understanding spatial relationships in 2D data for vision related tasks. Even though they are not designed to capture 1D temporal relationships, with TimeCaps we demonstrate that given the ability, capsule networks excel in understanding temporal relationships. To this end, we generate capsule…
Monotonic relationship found between in-distribution and out-of-distribution performance.
problem Understanding performance of machine learning models under distribution shifts.
method Analyzing ridge-regularized models and linear inverse problems under covariate shift.
result Monotonic relationship between in-distribution and out-of-distribution performance for certain models.
P2P lending activities have grown rapidly and have caused the huge and complex networks of debtor-creditor relationships. The aim of this study was to study the underlying structural characteristics of networks formed by debtor-creditor relationships. According attributes of P2P lending, this paper model the networks o…
Enhances topic-metadata relationship modeling using Bayesian methods.
problem Estimating relationships between latent topics and metadata in topic modeling.
method Proposes modifications to the method of composition, using Beta regression and a fully Bayesian approach.
result Improves quantification of uncertainty in topic-metadata relationships.
Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity." The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization…
By means of cohomology groups, we study relationships between equivariant gerbes with connection over a manifold with a Lie group action and gerbes with connection over the quotient space.
Supply Chain Management often requires independent organizations to work together to achieve shared objectives. This collaboration is necessary when coordinated actions benefit the group more than the uncoordinated efforts of individual firms. Despite the commonly reported benefits that can be gained in close relations…
Study predicts social relationships using triadic influence from social networks.
problem Difficulty in quantifying social relationships and their dynamics.
method Real social networks of 13 schools, neural networks, high-dimensional embedding.
result Triadic influence achieves highest accuracy in predicting student relationships.