This course is designed to provide 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.
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.
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 the urban domain.
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 structure in the urban context. 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
Introduction Accurate prediction of rental prices poses a significant challenge in dynamic real estate markets such as Amsterdam. Our research project explores the use of graph-based machine learning to improve the accuracy of such predictions. This methodology could be of interest to various actors in the real estate sector, including brokers, investors, and urban planners. … Read more
Who Why: For the Amenities, we selected the following due to its overall correlation with residential real estate and we anticipate that it will give a good indication of how the properties would be priced. By creating bounding boxes around the amenities, with a radius of 1km, we ensure that as much of the graph … Read more
Our goal was to predict the relationship between the tourist activity zones and the airbnb rentals. Tourism is vital to Spain’s economic growth, with Barcelona as a key contributor, accounting for over 12% of the country’s GDP. TOURIST ANALYSIS AND DATA Tourist spots and zones can be broadly classified to tourist amenities – transits, bus … Read more
Dataset Our database contains more than 181,000 rows, each with comprehensive information. The primary database includes 17 variables, though not all are necessary for our analysis. The most crucial data points are location (latitude and longitude), type of food establishment, type of inspection, inspection results, and risk level. As the person who uploaded the database … Read more
Rethinking Urban Spaces Parks and green areas are critical in cities as they provide spaces for people to meet, interact, and find a social life. They contribute significantly to the mental and physical well-being of residents, offering a natural respite from the urban hustle. Project Summary: According to google reviews, the most important factor for … Read more
For the last two decades, Mexico has grappled with an escalating wave of violence that has put the entire nation on edge, but it is particularly alarming and dangerous simply for being a woman. This alarming trend, characterized by uncontrolled levels of violence and fueled by a deeply ingrained misogynistic culture, underscores the urgent need … Read more
Greater Manchester, one of the largest and most vibrant urban centers in the UK, is characterized by its substantial student population and a dynamic economic landscape. With approximately 120,670 university students during the 2021/22 academic year and a significant number of these individuals studying at the University of Manchester and Manchester Metropolitan University, the region … Read more
is a Graph ML Project, to Predict the Potential of agricultural lands according to Soil meniral statistics, River water statistics and waste land types and proximity. Location: Fermanagh and Omagh, Northern Ireland. After extensive research on open-source data, we discovered that Ireland provides a wealth of information. We studied the historical land uses we found … Read more
Hypothesis The goal of this research was to investigate if open spatial data could predict Yelp ratings utilizing graph machine learning (GML) methods. We hypothesize that urban phenomena, events, and objects will indicate customer reviews and popularity, and therefore, could be used predict ratings. In particular, we perform edge classification using the DGL library. For … Read more
To predict whether a location in Barcelona is a key tourist site or not using graph data and specific contextual features, including the impact of the PEUAT plan on regulating tourist accommodations. The features explored and the URL selected on the Barcelona open data are score or indicator related to the tourism plan PEUAT, Proportion … Read more
This post describes the development of a method to predict metro station locations using Graph Neural Networks (GNNs). Our journey began with a challenge familiar to urban planners: how to strategically place new metro stations to optimize transportation networks. In this case the city of Stockholm was used as testbed. The Challenge of Data Imbalance: … Read more
