IEEE

Influence Maximization Under Network Data Distortion: A Max-Min Ant System with Multi-Graph Random Walk Approach

Abstract: Influence maximization (IM) aims to select a seed set of users that maximizes the expected influence spread and is a fundamental problem in social network analysis. The dynamic and complex ...
techxplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
GitHub

network-reconnaissance

gather information about a target website using various tools and techniques to perform subdomain enumeration, directory enumeration, port scanning and service enumeration, vulnerability scanning, web ...
Hosted on MSN

Neural network Python from scratch with softmax

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...
Hosted on MSN

Build a deep neural network from scratch in Python

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
C&EN

Multilevel Fusion Graph Neural Network for Molecule Property Prediction

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
InfoWorld

Air-gapped Python: Setting up Python without a net(work)

Installing Python and related applications on a system without a network connection isn’t easy, but you can do it. Here’s how. The vast majority of modern software development revolves around one big ...
The Hacker News

Python-Based Malware Powers RansomHub Ransomware to Exploit Network Flaws

Cybersecurity researchers have detailed an attack that involved a threat actor utilizing a Python-based backdoor to maintain persistent access to compromised endpoints and then leveraged this access ...
www.cs.cmu.edu

SoS Lower Bounds for Coloring Random Graphs

Abstract: Coloring for random graph from G(n,1/2) is a classic example exhibiting an Information v. Computation gap: it has chromatic number of Theta(n/log n) w.p. 1-o(1) while the best efficiently ...