Concept

Node (computer science)

Related publications (32)

Graph neural networks for dynamic modeling of roller bearings

Machine learning has paved the way for the real-time monitoring of complex infrastructure and industrial systems. However, purely data-driven methods have not been able to learn the underlying dynamics and generalize them to operating conditions that have ...

PHM Society2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

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 ...

EPFL2023

A Delay and Power Efficient Voltage Level Shifter with Low Leakage Power

Alexandre Schmid, Mehdi Saberi

This paper presents a new power-efficient and high-speed voltage level shifter. In the proposed structure, the existing contentions at the internal nodes are reduced using auxiliary transistors and feedback networks, leading to a significant reduction in t ...

New York2023

The power of adaptivity in source identification with time queries on the path

Patrick Thiran, Gergely Odor, Victor Cyril L Lecomte

We study the problem of identifying the source of a stochastic diffusion process spreading on a graph based on the arrival times of the diffusion at a few queried nodes. In a graph

G=(V,E)

, an unknown source node

v^* \in V

is drawn uniformly at random, ...

2022

Tree in Tree: from Decision trees to Decision Graphs

Mahsa Shoaran, Bingzhao Zhu

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional dec ...

Curran Associates, Inc, (NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems)2021

Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]

Dimitri Nestor Alice Van De Ville, Negar Kiyavash

The articles in this special section focus on graph signal processing. Generically, the networks that sustain our societies can be understood as complex systems formed by multiple nodes, where global network behavior arises from local interactions between ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Data stream statistics over sliding windows: How to summarize 150 Million updates per second on a single node

Grigorios Chrysos, Dionysios Pnevmatikatos, Odysseas Papapetrou

Traditional data management systems map information using centralized and static data structures. Modern applications need to process in real time datasets much larger than system memory. To achieve this, they use dynamic entities that are updated with str ...

2019

ScienceWISE: Topic Modeling over Scientific Literature Networks

Paolo De Los Rios, Alessio Vincenzo Cardillo, Andrea Martini

We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of te ...

2017

Models and Algorithms in Biological Network Evolution with Modularity

Min Ye

Networks are commonly used to represent key processes in biology; examples include transcriptional regulatory networks, protein-protein interaction (PPI) networks, metabolic networks, etc. Databases store many such networks, as graphs, observed or inferred ...

EPFL2017

Notes on Majority Boolean Algebra

Giovanni De Micheli, Mathias Soeken, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù, Anupam Chattopadhyay

A Majority-Inverter Graph (MIG) is a homogeneous logic network, where each node represents the majority function. Recently, a logic optimization package based on the MIG data-structure, with 3-input majority node (M3) has been proposed [2], [30]. It is dem ...

IEEE2016

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