awesome-network-analysis - A curated list of awesome network analysis resources.

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An awesome list of resources to construct, analyze and visualize network data. Inspired by Awesome Deep Learning, Awesome Math and others.



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awesome-capsule-networks - A curated list of awesome resources related to capsule networks


A curated list of awesome resources related to capsule networks maintained by AI Summary. Please pull a request if you are aware of additional resources.

BCN Complex Networks Library

  •    C

bcnnet-lib (barcelona complex networks library) is a library to support simulations and information analysis of complex networks. It contains a number of algorithms used in the fields of social networks, multi-agent simulation and network analysis.

awesome-very-deep-learning - 🔥A curated list of papers and code about very deep neural networks


awesome-very-deep-learning is a curated list for papers and code about implementing and training very deep neural networks. Value Iteration Networks are very deep networks that have tied weights and perform approximate value iteration. They are used as an internal (model-based) planning module.

Social Networks Visualizer

  •    Java

Social Networks Visualizer (SocNetV) is a social network analysis tool. You can draw a network (graph) or load an existing one (GraphML, UCINET, Pajek, etc), compute statistics, centralities, and apply various layout algorithms (i.e. spring-embedder)


  •    Javascript

Cytoscape.js is a fully featured graph theory library. Do you need to model and/or visualise relational data, like biological data or social networks? If so, Cytoscape.js is just what you need. Cytoscape.js contains a graph theory model and an optional renderer to display interactive graphs. This library was designed to make it as easy as possible for programmers and scientists to use graph theory in their apps, whether it's for server-side analysis in a Node.js app or for a rich user interface.

SocialVPN - P2P VPN that connects you to your friends computer

  •    C

SocialVPN is an open-source IPOP-based virtual network that connects your computers privately to your friends’ computers. It automatically maps online social network relationships using Jingle and XMPP to create your own user-defined peer-to-peer VPNs – with no hassle, and supporting unmodified TCP/IP applications.

osmnx - OSMnx: Python for street networks

  •    Python

Retrieve, construct, analyze, and visualize street networks from OpenStreetMap: full overview. You can just as easily download and work with building footprints, elevation data, street bearings/orientations, and network routing.

Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources


This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.

awesome-deep-learning-music - List of articles related to deep learning applied to music

  •    TeX

By Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

zarp - Network Attack Tool

  •    Python

Zarp is a network attack tool centered around the exploitation of local networks. This does not include system exploitation, but rather abusing networking protocols and stacks to take over, infiltrate, and knock out. Sessions can be managed to quickly poison and sniff multiple systems at once, dumping sensitive information automatically or to the attacker directly. Various sniffers are included to automatically parse usernames and passwords from various protocols, as well as view HTTP traffic and more. DoS attacks are included to knock out various systems and applications. These tools open up the possibility for very complex attack scenarios on live networks quickly, cleanly, and quietly. The long-term goal of zarp is to become the master command center of a network; to provide a modular, well-defined framework that provides a powerful overview and in-depth analysis of an entire network. This will come to light with the future inclusion of a web application front-end, which acts as the television screen, whereas the CLI interface will be the remote. This will provide network topology reports, host relationships, and more. zarp aims to be your window into the potential exploitability of a network and its hosts, not an exploitation platform itself; it is the manipulation of relationships and trust felt within local intranets. Look for zeb, the web-app frontend to zarp, sometime in the future.

Wireshark - Network Traffic Analyzer

  •    C

Wireshark is a network traffic analyzer, or "sniffer", for Linux, macOS, *BSD and other Unix and Unix-like operating systems and for Windows. It uses Qt, a graphical user interface library, and libpcap and npcap as packet capture and filtering libraries.

t81_558_deep_learning - Washington University (in St

  •    Jupyter

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

HumHub - Open Source Social Network

  •    PHP

HumHub is a feature rich and highly flexible OpenSource Social Network Kit written in PHP. It provides support to build Social Intranets, Enterprise Social Networks, Private Social Networks. Its social features include Commenting, Like, Following, Mentioning, Tags, OEmbed Support. The communication in HumHub works with spaces. A space can literally be anything, a project, a group or just a simple topic. For every space you can invite multiple users and make up your own access rights and rules.

NS 3 - Network Simulator

  •    C

Ns is a discrete event simulator targeted at networking research. Ns provides substantial support for simulation of TCP, routing, and multicast protocols over wired and wireless (local and satellite) networks.

regl-cnn - Digit recognition with Convolutional Neural Networks in WebGL

  •    Javascript

GPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

  •    Python

SuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of SuperPoint keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair. Full paper PDF: SuperGlue: Learning Feature Matching with Graph Neural Networks.

dash-cytoscape - Interactive network visualization in Python and Dash, powered by Cytoscape.js

  •    Python

A Dash component library for creating interactive and customizable networks in Python, wrapped around Cytoscape.js. If you want to install the latest versions, check out the Dash docs on installation.

LightNet - LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

  •    Python

This repository contains the code (in PyTorch) for: "LightNet: Light-weight Networks for Semantic Image Segmentation " (underway) by Huijun Liu @ TU Braunschweig. Semantic Segmentation is a significant part of the modern autonomous driving system, as exact understanding the surrounding scene is very important for the navigation and driving decision of the self-driving car. Nowadays, deep fully convolutional networks (FCNs) have a very significant effect on semantic segmentation, but most of the relevant researchs have focused on improving segmentation accuracy rather than model computation efficiency. However, the autonomous driving system is often based on embedded devices, where computing and storage resources are relatively limited. In this paper we describe several light-weight networks based on MobileNetV2, ShuffleNet and Mixed-scale DenseNet for semantic image segmentation task, Additionally, we introduce GAN for data augmentation[17] (pix2pixHD) concurrent Spatial-Channel Sequeeze & Excitation (SCSE) and Receptive Field Block (RFB) to the proposed network. We measure our performance on Cityscapes pixel-level segmentation, and achieve up to 70.72% class mIoU and 88.27% cat. mIoU. We evaluate the trade-offs between mIoU, and number of operations measured by multiply-add (MAdd), as well as the number of parameters.

grenade - Deep Learning in Haskell

  •    Haskell

Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. And that's it. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself.

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