Stephen Bonner

Using Hadoop To Implement a Semantic Method Of Assessing The Quality Of Research Medical Datasets

In this paper a system for storing and querying medical RDF data using Hadoop is developed. This approach enables us to create an inherently parallel framework that will scale the workload across a cluster. Unlike existing solutions, our framework uses highly optimised joining strategies to enable the completion of eight separate SPAQL queries, comprised of over eighty distinct joins, in only two Map/Reduce iterations. Results are presented comparing an optimised version of our solution against Jena TDB, demonstrating the superior performance of our system and its viability for assessing the quality of medical data.

Causal embeddings for recommendation

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.

GFP-X: A parallel approach to massive graph comparison using spark

The problem of how to compare empirical graphs is an area of great interest within the field of network science. The ability to accurately but efficiently compare graphs has a significant impact in such areas as temporal graph evolution, anomaly detection and protein comparison. The comparison problem is compounded when working with massive graphs containing millions of vertices and edges. This paper introduces a parallel feature extraction based approach for the efficient comparison of large unlabelled graph datasets using Apache Spark. The approach acts by producing a ‘Graph Fingerprint’ which represents both vertex level and global level topological features from a graph. By using Spark we are able to efficiently compare graphs considered unmanageably large to other approaches. The runtime of the approach is shown to scale sub-linearly with the size and complexity of the graphs being fingerprinted. Importantly, the approach is shown to not only be comparable to existing approaches, but on when comparing topology and size, more sensitive at detecting variation between graphs.

Evaluating the quality of graph embeddings via topological feature reconstruction

In this paper we study three state-of-the-art, but competing, approaches for generating graph embeddings using unsupervised neural networks. Graph embeddings aim to discover the ‘best’ representation for a graph automatically and have been applied to graphs from numerous domains, including social networks. We evaluate their effectiveness at capturing a good representation of a graphs topological structure by using the embeddings to predict a series of topological features at the vertex level. We hypothesise that an ‘ideal’ high quality graph embedding should be able to capture key parts of the graphs topology, thus we should be able to use it to predict common measures of the topology, for example vertex centrality. This could also be used to better understand which topological structures are truly being captured by the embeddings. We first review these three graph embedding techniques and then evaluate how close they are to being ‘ideal’. We provide a framework, with extensive experimental evaluation on empirical and synthetic datasets, to assess the effectiveness of several approaches at creating graph embeddings which capture detailed topological structure.

Exploring the Evolution of Big Data Technologies

This chapter explores the rise of “big data” and the computational strategies, both hardware and software, that have evolved to deal with this paradigm. Starting with the concept of data-intensive computing, the different facets of data processing like Map/Reduce, Machine Learning, and Streaming data are explored. The evolution of different frameworks such as Hadoop and Spark are outlined and an assessment of the modular offerings within the frameworks is compared with a detailed analysis of the different functionalities and features. The hardware considerations required to move from compute-intensive to data-intensive are outlined along with the impact of cloud computing on big data. The chapter concludes with the upcoming developments in the near future for big data and how this computing paradigm fits into the road to exascale.

Deep topology classification: A new approach for massive graph classification

The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the classification of the entire graph. Whilst there is considerable work on the first problem, the efficient and accurate classification of massive graphs into one or more classes has, thus far, received less attention. In this paper we propose the Deep Topology Classification (DTC) approach for global graph classification. DTC extracts both global and vertex level topological features from a graph to create a highly discriminate representation in feature space. A deep feed-forward neural network is designed and trained to classify these graph feature vectors. This approach is shown to be over 99% accurate at discerning graph classes over two datasets. Additionally, it is shown to be more accurate than current state of the art approaches both in binary and multi-class graph classification tasks.