Project Abstract

Over the recent years, Graph Neural Networks (GNNs) have become increasingly popular in supplementing traditional network analytic techniques. The capstone project will seek proof-of-concept applications on the GNNs and the Hierarchical GNNs in particular to diverse cases of urban network analytics ranging from urban mobility and transportation networks, social media analytics, social networks, urban infrastructure, environmental sensing and beyond.

Project Description & Overview

A city is an interconnected complex system and requires network analysis to be understood. Over the recent years, Graph Neural Networks (GNNs) have become increasingly popular in supplementing traditional network analytic techniques. At the same time, many conventional approaches in network science efficiently utilize the hierarchical approaches to account for the hierarchical organization of the networks, and recent works emphasize their critical importance. Our lab is working on a novel model of the Hierarchical GNN, accounting for the hierarchical organization of the urban network and connecting the dots between the traditional network science approaches, vanilla Neural Network, and the GNN architectures. This Capstone project will seek proof-of-concept applications on the GNNs and the Hierarchical GNNs to diverse cases of urban network analytics ranging from urban mobility and transportation networks, social media analytics, social networks, urban infrastructure, environmental sensing and beyond. The practical applications may range from predictive modeling and detection of patterns, impacts, and emergent phenomena in urban mobility and social interactions, urban zoning and regional delineation, classification of urban actors and locations, detecting critical bottlenecks in urban infrastructure, data verification and extrapolation in sensing urban environment and/or quantifying population exposure to urban stressors.

Datasets

LEHD, NYC TLC and other taxies/FHV, CitiBike, public transit, Twitter, migration data, financial

Competencies

• Network analysis, neural networks, pytorch or tensorflow, natural language processing and/or social media analytics experience is a plus (optional)
• Background in urban transportation, planning, environmental sensing is a plus (optional)

Learning Outcomes & Deliverables

1. Learn how to train supervised and unsupervised graph neural network models;
2. Explore applicability of graph neural networks for urban network analysis;
3. Publication in multidisciplinary, urban or computer science venues.

Students

Jingxin He, Jiale Li, Vinita Milind Wagh, Ke Zhao