Modeling complex networks: An implementation based on Python+NetworkX

:bar_chart:复杂网络建模课程设计. The project of modeling of complex networks course.

Code for the networks chapters in Sayama's Introduction to the Modeling and Analysis of Complex Systems, updated for the networkx 2.x library.

Churn modeling using Artificial Neural Networks (ANNs) involves building a predictive model to identify customers who are likely to churn (stop using a service or product). ANNs, a type of machine learning model inspired by the human brain's neural structure, can be effective for capturing complex patterns in data.

This system collects various existing illegal transaction data, performs community analysis through complex networks based on the original transaction data, traces two- and three-layer networks, and models cryptocurrency transaction relationships using tracing algorithms, traceability algorithms, and traceability layer analysis. Based on the modeling, feature extraction, analysis of temporal features and network features are performed on the data to build a dataset for predicting illegal trading behavior using machine learning models. #TODO The system combines front and back-end, and the results can be visualized in the form of pictures for monitoring and viewing the analysis of current cryptocurrency trading behavior on the Web side.

Final project for the course Modeling and Data Analysis in Complex Networks (CS4195) at TU Delft

Customer churn modeling involves predicting which customers are likely to leave a service or business. This is crucial for businesses to retain customers and improve satisfaction. Using artificial neural networks (ANNs) can enhance prediction accuracy due to their ability to model complex relationships in data.

This is the project that we did for the "Operational research: theory and applications" course in the Polytechnic University of Turin (Polito). In this course we used Python and XPress-Mosel. The main aim of the course is to provide students with theoretical and operational tools for modeling and solving Operations Research and Optimization problems usually met in the design and the management of complex infrastructures such as computer networks. Operations Research deals with the mathematical modeling of complex problems and related solution algorithms.

Computational Science and Engineering Master project on reduced order modeling of complex nonlinear systems using neural networks

Course: 02822 Social data modellering The course objective is to enable the students to create visualizations of complex data sets and to apply common strategies for detecting sentiment in media (e.g. text, music, images, etc). The students will be able to apply and assess simple machine learning techniques (e.g. non-negative matrix factorization, document classification, decision trees), as well as analyze the dynamics of complex networks. Media is described using features, extracted based on high level programming languages and APIs. The course provides a conceptual basis for modeling social data.

syllabus for Modeling Dynamical Systems on Complex Networks

A package in R for modeling infectious diease transmission on population with social and biological heterogenity, based on SIR-compartmental complex epidemiology random networks with percolation process method and its modifications. Developing, optimizing and testing in progress now!

Modelin some real-world networks with common complex network models (e.g. Barabasi-Albert, Watts-Strogatz, Holme-and-Kim, ...).

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Assignment for the course "Modeling and Data Analysis in Complex Networks"

A library capable of modeling complexed-valued neural networks in Rust.

A framework for modeling information diffusion, polarization, synchronization, and echo chamber formation in complex networks.

First assignment of the course EE4389 Modeling and Data Analysis in Complex Networks (2016-2017 Q3) at @TU Delft

Datasets for "Quantum-inspired modeling of social impact in complex networks with artificial LLM-based agents" Scirentific Reports 2025 article.

pharo-networks is a powerful complex network library designed to assist researchers, engineers, and data scientists in analyzing and modeling complex systems. Built upon state-of-the-art methodologies and algorithms, pharo-networks offers a comprehensive suite of tools and functionalities for the analysis and simulation of complex networks.

Complex system modeling frameworks: cellular automata for modeling interactions of cells, graphs networks for traffic simulations, forest fire spread simulations under varying conditions and simulated annealing (for mathematical optimization)

A dual-architecture (Go+Python) Agent-Based Modeling framework for simulating new product diffusion in complex networks using Large Language Models (LLMs).

Community Influence Analysis for Network Data focuses on modeling influence evaluation in complex networks, with specific applications in two domains: music data from Spotify and stock market data.

This project implements collaborative filtering using Graph Neural Networks (GNNs) on the MovieLens dataset. By modeling user-item interactions as a graph, the project leverages the power of GNNs to capture complex relationships and make more accurate recommendations.

TransitGraphAI explores the intersection of transport network modeling, graph generative models, and reinforcement learning for time-dependent routing. The goal is to build and experiment with AI systems capable of understanding, generating, and navigating complex multimodal public transport networks.

Artificial neural networks are used in sequence and pattern recognition systems, data processing, robotics, modeling, etc. ANN acquires knowledge from their surroundings by adapting to internal and external parameters and they solve complex problems which are difficult to manage.

A powerful Transformer architecture built from scratch by Prajwal for sequence modeling tasks. This model captures complex patterns in data using multi-head self-attention, layer normalization, and feedforward networks. It’s ideal for NLP, classification, translation, and generative tasks.

The project explores physics simulation with Graph Networks, inspired by DeepMind’s 2021 paper. It uses a Physics Graph Network (PGN) to address traditional simulators' limitations, effectively modeling complex interactions and simulating systems like particle dynamics, cloth, and fluid mechanics.

This queue network simulator enables detailed modeling of complex network topologies with variable routing, ideal for analyzing and optimizing computer networks and logistical systems. Easily configure via a YAML file and explore detailed statistics for each queue, including state probabilities and loss rates.

Computational Application of Radial Basis Function Neural Networks (RBFNN) I employ radial basis functions in hidden layers, efficiently modeling complex nonlinear relationships in data. Their unique architecture enables accurate function approximation, classification, and regression, making them versatile and effective across multiple domains.