By the end of this course, students will have a solid foundation in graph machine learning and be able to apply these techniques to analyse and model complex data with graph structure.


Syllabus

Graph Machine Learning 

MaCAD Digital Tools for GRAPH MACHINE LEARNING SEMINAR

In today’s data-driven world, graphs have emerged as a powerful tool for modelling complex relationships and data structures. Graph machine learning, also known as graph-based machine learning or graph analytics, is a rapidly growing field that leverages graph theory, network analysis, and machine learning techniques to extract valuable insights and make predictions from graph-structured data.

Source: CONFIGURABLE TOPOLOGIES – Naitik Sharma. MaCAD 21-22 Thesis

This course is designed to provide you with a comprehensive understanding of graph machine learning, from fundamental concepts to some advanced techniques, and will equip you with the necessary knowledge and skills to effectively leverage graph-based approaches in many aspects of design.

Throughout this course, we will cover a wide range of topics, including graph representation, graph embedding, graph clustering, graph neural networks (GNNs), among others. We will also discuss various applications of graph machine learning within and outside the design domain and the generation of data for such purposes. 

Through a combination of lectures, hands-on coding exercises, and world case studies we will aim to solidify your understanding of these concepts and techniques. You will also have the opportunity to work with popular graph machine learning libraries and tools, such as NetworkX, DGL and Tensorflow in order to gain practical experience in implementing graph machine learning models and solving design problems.

By the end of this course, you will have a solid foundation in graph machine learning and be able to apply these techniques to analyse and model complex data with graph structures. We hope you will enjoy this exciting journey to unlock the power of graphs and take your machine learning skills to new heights! 

Learning Objectives 

  • Learn the fundamentals of graphs
  • Learn the fundamentals of Graph Machine Learning
  • Understand and evaluate Graph Machine Learning models
  • Explore different applications of GraphML within the design domain

Faculty


Faculty Assistants


Projects from this course

Vastu in Graph Machine Learning

CONCEPT Vastu Shastra is a traditional Indian architectural science that provides a wide range of guidelines for design, layouts and construction of buildings. Vastu Shastra is based on the principles of ancient Indian texts and it aims to create harmonious and balanced living spaces that promote physical, mental and spiritual well being. WORKFLOW DESIGN PROCESS … Read more

Museoflow

A Graph Machine Learning Approach for Classifying Museums Layouts Based on their Circulation Patterns and Gallery Experiences. This project aims to use graph machine learning to evaluate and classify museum layouts and experiences. By leveraging the structure and relationships within a museum’s most contributing elements, such as galleries and circulation. The project aims to identify … Read more

Wildlife Corridors

Our project focuses on modeling and predicting habitat fragmentation avoidance mechanisms using graphs. We investigate wildlife corridors in Australia, leveraging graph-based approaches to create coherent passages and connect forest areas. By analyzing satellite images, road data, and other metrics, we construct and evaluate graph models that incorporate forest containment, node characteristics, and connectivity. Our goal … Read more

Airports as Graphs

Introduction: Airports, intricate systems encompassing numerous services and pathways within sprawling infrastructures, face the challenge of optimizing the flow towards departure gates. In this study, we delved into the possibility of filtering out non-essential activities and prioritizing efficient movement within airports. To gain insights, we focused on Oslo Gardermoen airport, a moderate-sized airport with a … Read more

Egress Prediction

This initiative is oriented towards the practical undertaking of identifying egress points within a floor plan. We have analyzed three typologies of a structure akin to multi-family dwellings or hotel-style establishments. After a thorough engagement with the process, there are subsequent measures to be considered, which we intend to elaborate upon in our final review. … Read more

Optimum Location for Medical Center

Introduction In some cities one of the important issues that needs to be resolved is the accessibility to a medical center in a short period of time to save people’s lives. Furthermore, in an emergency situations the ambulance should be able to reach to the patient and hospital as fast as it can without any … Read more

Cats & Books

From WFC to Graph ML WFC is an algorithm by Maxim Gumin using tile-based stochastic (random) aggregation. One of the most well known uses of WFC is the game Townscraper by Oskar Stalberg which is an indie town-builder game with 320k downloads. Lectures from Oskar Stalberg as well as YouTube videos form DV Gen are … Read more

EASE GUIDE

 ‘Navigating Future Convenience’ PROBLEM STATEMENT The dataset contains information about previous trips taken by users, where each trip is represented as a node in the graph. The edges between the nodes represent connections between destinations visited by the same user. Additionally, the dataset includes attributes such as location details, amenities, time of day, activity type, … Read more

Routable

An Urban AI graft project based in Barcelona. In 2022 across both England and Wales the proportion of people that have some form of disability was a staggering 17.8% or nearly 10.4 million people. Our project is focused on urban mobility and aims to create accessibility for current routing applications have limited use for people … Read more

Floor Plan Evaluator

Floor Plan Evaluator using graph machine learning Project Workflow Problem definition & objective formulation With the growing pressure on cities available spaces for vertical or horizontal expansion, it’s crucial to make the most efficient use of the available spaces without compromising the space qualities. Conventional work flow of floor plan design is rigid regarding evaluating … Read more