Proposes a method to infer networks using node-specific degree priors.
problem Network inference from partially observed edges.
method Formulates network inference as a matrix completion problem regularized by a node-specific degree prior derived from observed edges.
result Improves network recovery error bound compared to previous methods.
Dynamic network model forecasts interbank market link formation.
problem Forecasting interbank market link formation with time-varying topologies.
method Dynamic network model with past link existence and node-specific latent variables. Markov dynamics and EM algorithm for estimation.
result Forecasting future link presence and recognition of preferential lending.
A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, …
AGCRN forecasts traffic using adaptive graph and recurrent learning.
problem Forecasting traffic dynamics with complex spatial and temporal correlations.
method Adaptive Graph Convolutional Recurrent Network (AGCRN) with Node Adaptive Parameter Learning (NAPL) and Data Adaptive Graph Generation (DAGG).
result AGCRN outperforms state-of-the-art models without pre-defined graphs.
New graph model relaxes acyclic constraint for better flexibility.
problem Graphical model constraints limit flexibility.
method Introduces new graphical models with relaxed constraints.
result New models allow directed cycles and up to two edges between nodes.
Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which are widely used to model social and biological networks. The prior not only favors a desirable global node degree distribution, but also ta…
New decentralized KRR algorithm adapts to node-specific data.
problem Consistent node-specific data in decentralized KRR.
method Data-dependent random features for adaptive RF generation.
result Average regression accuracy improved by 25.5% across six datasets.
JSRT improves regression tree performance by incorporating global node information.
problem Regression tree performance relies on local node means, ignoring global node information.
method Proposes JSRT by integrating global mean information from different nodes.
result Demonstrates superior performance and efficiency compared to other regression tree methods.
A graph-based method for two-sample testing across connected nodes.
problem Identifying nodes where two probability distributions differ significantly.
method Collaborative non-parametric two-sample testing (CTST) framework.
result CTST outperforms independent node tests by leveraging graph structure.
A new framework detects abnormal node-level communication activity in networks.
problem Detecting abnormal communication volume at node-level in communication networks.
method Probabilistic framework using clique streams and non-parametric regression.
result The proposed approach outperforms in real-world and synthetic data.
MEG models for dynamic networks estimate dependencies and shared latent space relationships.
problem Modeling dynamic networks with shared latent space relationships and dependencies.
method MEG combines mutually exciting point processes and latent space models to estimate node-specific parameters and unobserved edges.
result MEG models can estimate intensities for unobserved edges, useful for anomaly detection in real-world applications.
We address a fundamental problem that is systematically encountered when modeling complex systems: the limitedness of the information available. In the case of economic and financial networks, privacy issues severely limit the information that can be accessed and, as a consequence, the possibility of correctly estimati…
New method learns discrete graphical models efficiently.
problem Learning discrete graphical models with minimal data.
method Interaction Screening framework for fully general discrete factor models.
result Provably learns model structure and parameters with rigorous guarantees.
Accelerates Bayesian optimization of function networks with partial evaluations.
problem Optimizing expensive-to-evaluate function networks with varying node costs.
method Proposes an accelerated algorithm that uses global Monte Carlo simulations to select node-specific candidate inputs.
result Achieves up to a 16x speedup over the original p-KGFN algorithm while maintaining competitive query efficiency.
BeGIN benchmarks GNNs for instance-dependent label noise in graphs.
problem Instance-dependent label noise in graph data.
method BeGIN introduces a benchmark with various noise types and evaluates noise-handling strategies across GNN architectures.
result Challenges of instance-dependent noise, especially LLM-based corruption, and the importance of node-specific parameterization.
Predicts Yelp star reviews using deep learning and network structure.
problem Predicting Yelp star reviews based on network structure and features.
method Compared multiple models including deep learning on network and item features.
result Deep learning models combining node-level and network features outperform others.
This paper models how features influence event triggers in high-dimensional networks.
problem Estimating context-dependent networks in high-dimensional marked point processes.
method Leveraging compositional time series and regularization methods, the paper considers autoregressive multinomial and logistic-normal models for network estimation.
result The logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories.
The paper addresses differentially private learning for neural networks, focusing on risk bounds and algorithm feasibility.
problem Achieving differentially private learning for neural networks with theoretical guarantees.
method Developed algorithms and theoretical analysis for differentially private stochastic optimization of neural networks.
result Established theoretical bounds for excess population risk in differentially private learning of neural networks.
FairDTD improves fairness in GNNs by distilling dual teacher knowledge, balancing utility and bias.
problem Bias in GNN predictions due to sensitive attributes.
method Dual-Teacher Distillation with a causal graph model, feature and structure teachers, and graph-level distillation.
result Achieves optimal fairness while preserving high model utility.
FI-GRL learns graph node representations efficiently and generalizes to unseen nodes.
problem Transductive graph representation learning requires all nodes to be known, limiting generalization.
method FI-GRL uses random projection to preserve graph structure and feature extraction via SVD.
result FI-GRL achieves accurate representations for seen nodes and generalizes to unseen nodes.
Graph-based LRE estimates likelihood-ratios collaboratively for nodes.
problem Comparing unknown pdfs at graph nodes with graph structure.
method Graph-based Relative Unconstrained Least-squares Importance Fitting (GRULSIF).
result Collaborative estimation improves performance compared to independent methods.
GT-SARAH optimizes decentralized non-convex problems with recursive variance reduction.
problem Decentralized non-convex optimization of N functions over a network. method Stochastic first-order gradient method with SARAH variance reduction and gradient tracking.
result Achieves ε-accurate first-order stationary point with improved gradient complexity.