Chidubem Iddianozie’s research while affiliated with University College Dublin and other places

In this paper, we study transferable graph neural networks for street networks. The use of Graph Neural Networks in a transfer learning setting is a promising approach to overcome issues such as the lack of good quality data for training purposes. With transfer learning, we can fine-tune a model trained on a rich sample of data before applying to a task with limited examples. Specifically, we focus on the open research problem of inferring the attributes of a street network as a node classification task. An attribute contains descriptive information about a street segment such as the street type. We propose and develop a neural framework capable of learning from multiple street networks to infer the semantics of another street network. Different from previous studies, we are the first to address this problem by learning from more than one street network using graph neural networks. We empirically evaluate our framework on multiple large real-world networks. Our evaluations show that while state-of-the-art methods can be negatively impacted by naive transfer learning, our framework consistently mitigates this phenomenon, with up to a 10% gain in mean transfer accuracy.

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these assumptions may not be sufficiently held. In this article, we study the problem of graph learning on spatial graphs. Particularly, we focus on transductive learning methods for the imbalanced case. Given the nature of these graphs, we hypothesize that taking the global structure of the graph into account when aggregating local information would be beneficial especially with respect to generalisability. Thus, we propose a novel approach to training GNNs for these type of graphs. We achieve this through a sampling technique: Structure-Aware Sampling (SAS), which leverages the intra-class and global-geodesic distances between nodes. We model the problem as a node classification one for street networks with high variance between class sizes. We evaluate our approach using large real-world graphs against state-of-the-art methods. In the majority of cases, our approach outperforms traditional methods by up to a mean F1-score of 20%.

Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the interactions among these devices. Nonetheless, automatically inferring the semantics of these devices using data-driven methods provides an ideal solution to the problems brought about by this heterogeneity. In this paper, we undertake a multi-dimensional study to address the problem of inferring the semantics of IoT devices using machine learning models. Using two datasets with over 67 million data points collected from IoT devices, we developed discriminative models that produced competitive results. Particularly, our study highlights the potential of Image Encoded Time Series (IETS) as a robust alternative to statistical feature-based inference methods. Leveraging just a fraction of the data required by feature-based methods, our evaluations show that this encoding competes with and even outperforms traditional methods in many cases.

Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the interactions among these devices. Nonetheless, automatically inferring the semantics of these devices using data-driven methods provides an ideal solution to the problems brought about by this heterogeneity. In this paper, we undertake a multi-dimensional study to address the problem of inferring the semantics of IoT devices using machine learning models. Using two datasets with over 67 million data points collected from IoT devices, we developed discriminative models that produced competitive results. Particularly, our study highlights the potential of Image Encoded Time Series (IETS) as a robust alternative to statistical feature-based inference methods. Leveraging just a fraction of the data required by feature-based methods, our evaluations show that this encoding competes with and even outperforms traditional methods in many cases.

The performance of a machine learning algorithm is dependent on the quality of the available data for model development. However, in practical situations, the availability of the data is variable and can be limited. This limitation creates a budget problem for data-driven techniques and the objective in such situations is to develop the best model given the available data. In this article, we examine the budgeted learning problem for spatial data within the urban context. We demonstrate the effectiveness of a novel approach for inferring the attributes of spatial data when the data for the model is budgeted. This is achieved using urban functions - which describe the designated use of a geographical space - to infer the types of streets in a city. We evaluated the approach by comparing the performance of the model using the data in each urban function (the budget) against the results from the aggregate of all the functions (all data). The results indicate that with our model, individual urban functions are sufficient to infer the type attributes of streets.

Advances in machine learning and the availability of spatial data have seen remarkable improvements in recent times. This parallel growth has influenced the increased application of traditional data mining techniques for knowledge discovery on spatial data. However, these techniques assume that the data is drawn from an independent and identical distribution whereas spatial data is inherently dependent and heterogeneous. This contradiction strongly suggests that a crass application of conventional data mining techniques to spatial data would be suboptimal. In this paper, we evaluate the relatedness of street networks using a transfer learning methodology within the formal contexts of spatial data. Adopting a statistical multi-measure, we analyze street networks from eight cities in an attempt to ascertain their similarities. We predict the street types using random forests and evaluate the accuracies as a function of transfer polarity. Positive transfer is when the transferred models perform better than the parent model or negative transfer when it is worse. With an overall average accuracy of 85%, our results show that it is possible to generalize machine learning models onto different domains and still produce excellent results. Also, we demonstrate that the improved or loss of model accuracy can be explained by the proportion of statistical similarity between the domains. This observation confirms that a measure of inter-domain similarity solely based on geo-political boundaries will be erroneous. The techniques we have described are a statistically sound foundation for analysis of similarities in the spatial context. It can be adopted towards understanding the extent of model generalization for spatial networks.