Deep MinCut: Learning Node Embeddings by Detecting Communities

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We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Urban proximity planning is foreseen as a solution to foster a “sustainable” city, including economic viability, environmental soundness and social inclusivity. This paper focuses on the inclusivity aspects by questioning the adoption of urban proximities: ...

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communicat ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation functio ...

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Chronic pain presents a tremendous personal, societal, and financial burden. Treatment options are often limited to managing symptoms as opposed to treating the causes, and the consequences of pharmacological treatments can be immensely harmful as evidence ...

In this work, we develop a new framework for dynamic network flow pro-blems based on optimal transport theory. We show that the dynamic multicommodity minimum-cost network flow problem can be formulated as a multimarginal optimal transport problem, where t ...

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. Howeve ...

A graph H is a minor of a second graph G if G can be transformed into H by two operations: 1) deleting nodes and/or edges, or 2) contracting edges. Coarse-grained reconfigurable array (CGRA) application mapping is closely related to the graph minor problem ...

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

Deep MinCut: Learning Node Embeddings by Detecting Communities

Chattez avec Graph Search

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Recovering Static and Time-Varying Communities Using Persistent Edges

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Planning urban proximities: An empirical analysis of how residential preferences conflict with the urban morphologies and residential practices

Unlabeled Principal Component Analysis and Matrix Completion

Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs

Equivariant Neural Architectures for Representing and Generating Graphs

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Enforcing Privacy in Distributed Learning With Performance Guarantees

A Stand-Alone Augmented Reality Intervention for Chronic Pain using Embodied Systolic Stimulation

Scalable Computation of Dynamic Flow Problems via Multimarginal Graph-Structured Optimal Transport

Privacy-Preserving Machine Learning on Graphs

Network Alignment With Holistic Embeddings

GRAMM: Fast CGRA Application Mapping Based on A Heuristic for Finding Graph Minors

Event-based sampled ECG morphology reconstruction through self-similarity

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Maximum Independent Set: Self-Training through Dynamic Programming