arXiv research

BGNN improves GNN by modeling interactions between neighbor nodes.

GTEA learns node representations in temporal interaction graphs.

LNPE enhances local connections in embeddings using extended neighbor propagation.

SPINEX improves clustering with explainable neighbors, outperforming other methods.

New method quantifies feature interactions in machine learning models.

Prior distributions of binarized natural images are learned by using a Boltzmann machine. According the results of this study, there emerges a structure with two sublattices in the interactions, and the nearest-neighbor and next-nearest-neighbor interactions correspondingly take two discriminative values, which reflect…

Processing data collected by a network of agents often boils down to solving an optimization problem. The distributed nature of these problems calls for methods that are, themselves, distributed. While most collaborative learning problems require agents to reach a common (or consensus) model, there are situations in wh…

Study extends deep learning theory to long-range spin systems.

A new framework for causal inference in networked settings.

Modeling interacting objects with latent Gaussian process ODEs.

A fast method for finding counterfactual explanations for decision forests.

HopGAT improves node classification in sparsely labeled graphs by learning from distant neighbors.

In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool for data exploratory analysis. The state-of-the-art data embedding (DE) methods of N-D data into 2-D (3-D) visually perceptible space (e.g.…

SPINEX improves time series forecasting with explainable neighbors.

HGNet improves GNNs' ability to handle long-range interactions in graphs.

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a $\mathbf{computational\; model}$ of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predi…

Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN, Fusion Glyph Network for Chinese NER. Except for adding glyph information, this method may also add extra interactive information with the fusion mechanis…

We present a model in which we investigate the structure and evolution of a random network that connects agents capable of exchanging wealth. Economic interactions between neighbors can occur only if the difference between their wealth is less than a threshold value that defines the width of the economic classes. If th…

Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labe…

Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. Such strategic interactions are often modeled as games played on networks, where an individual's payoff depends not only on her action but also on that of her neighbors. The current literature has large…

Convolutional Neural Networks (CNN) have been pivotal to the success of many state-of-the-art classification problems, in a wide variety of domains (for e.g. vision, speech, graphs and medical imaging). A commonality within those domains is the presence of hierarchical, spatially agglomerative local-to-global interacti…

We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games map the input context to outcomes by first condensing the input into private player types that specify the utilities, weighted interactions…

In this paper, we study time-varying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stocks interact with each other…

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance…

Scalable Gaussian Process Operator tackles high-dimensional PDEs.

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species in…

A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with near neighbor interactions. The evolution of each agent results from the competition between two factors: the agent's own tendency to grow and the environmen…

In 1967, Japanese physicist Morikazu Toda published the seminal papers exhibiting soliton solutions to a chain of particles with nonlinear interactions between nearest neighbors. In the decades that followed, Toda's system of particles has been generalized in different directions, each with its own analytic, geometric,…

AnomalyDAE detects anomalies in networks by learning cross-modality interactions.

We present a linear agent based model on brand competition. Each agent belongs to one of the two brands and interacts with its nearest neighbors. In the process the agent can decide to change to the other brand if the move is beneficial. The numerical simulations show that the systems always condenses into a state when…

FEALM learns features for better nonlinear DR of hidden patterns.

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter informati…

Interactive tool for better understanding t-SNE projections.

The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions,…

ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.

The paper introduces a new method to measure the shape relations between biological objects using r-parallel sets.

We propose a three-state microscopic opinion formation model for the purpose of simulating the dynamics of financial markets. In order to mimic the heterogeneous composition of the mass of investors in a market, the agent-based model considers two different types of traders: noise traders and contrarians. Agents are re…

Study nearest-neighbor radii under dependent sampling, finding they remain informative.

Deep nearest neighbors outperform self-supervised methods in anomaly detection.

Graph machine learning and Super-App data improve credit risk prediction for financial inclusion.

Proposes a graph dynamics prior for more accurate relational inference.

Characterizes Lebesgue points using nearest neighbor methods.

The paper explains how nearest neighbor methods succeed in prediction.

We consider a group of Bayesian agents who try to estimate a state of the world $θ$ through interaction on a social network. Each agent $v$ initially receives a private measurement of $θ$: a number $S_v$ picked from a Gaussian distribution with mean $θ$ and standard deviation one. Then, in each discrete time iteration,…

AWNN improves matrix completion by adaptively weighting nearest neighbors.

A new method uses nearest neighbors for importance weighting.

A framework for flagging content with limited data.

Proposes a graph learning framework for clustering and semi-supervised classification.