Metrics for Graph Comparison: A Practitioner's Guide

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees in data in these fields yields insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances (also known as $\lambda$ distances) and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies and different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and empirical datasets. We put forward a multi-scale picture of graph structure, in which the effect of global and local structure upon the distance measures is considered. We make recommendations on the applicability of different distance measures to empirical graph data problem based on this multi-scale view. Finally, we introduce the Python library NetComp which implements the graph distances used in this work

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein–ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method’s promise for lead hopping within or outside a protein target, directly based on binding site information

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Towards Data-Driven Large Scale Scientific Visualization and Exploration

Technological advances have enabled us to acquire extremely large datasets but it remains a challenge to store, process, and extract information from them. This dissertation builds upon recent advances in machine learning, visualization, and user interactions to facilitate exploration of large-scale scientific datasets. First, we use data-driven approaches to computationally identify regions of interest in the datasets. Second, we use visual presentation for effective user comprehension. Third, we provide interactions for human users to integrate domain knowledge and semantic information into this exploration process. Our research shows how to extract, visualize, and explore informative regions on very large 2D landscape images, 3D volumetric datasets, high-dimensional volumetric mouse brain datasets with thousands of spatially-mapped gene expression profiles, and geospatial trajectories that evolve over time. The contribution of this dissertation include: (1) We introduce a sliding-window saliency model that discovers regions of user interest in very large images; (2) We develop visual segmentation of intensity-gradient histograms to identify meaningful components from volumetric datasets; (3) We extract boundary surfaces from a wealth of volumetric gene expression mouse brain profiles to personalize the reference brain atlas; (4) We show how to efficiently cluster geospatial trajectories by mapping each sequence of locations to a high-dimensional point with the kernel distance framework. We aim to discover patterns, relationships, and anomalies that would lead to new scientific, engineering, and medical advances. This work represents one of the first steps toward better visual understanding of large-scale scientific data by combining machine learning and human intelligence

The architecture of transit: photographing incidents of sublimity in the landscapes of motorway architecture between the Alps and Naples

The aesthetics of motorway architecture has not received attention within theoretical photographic discourse and has never been the subject of an academic photographic research project. This project begins from the understanding of the motorway as one continuous piece of architecture that crosses international boundaries on its route across Europe – an architecture so large that it cannot be perceived in its entirety. As a research-by-practice PhD, photography is used to identify and record incidents of the sublime in the route of the motorway. The photographs are produced with a large field study from the Swiss Alps to Naples, where numerous complex topographical and spatial conditions are found. This results in incidents of the sublime within its architecture when the motorway is forced to negotiate these conditions during its route. The research domain was chosen for its significance within the history of art and literature in European cultural history. Travelling in these regions was and is strongly related to the development of cultural concepts of the sublime. The questions that this research investigates are: Is it possible to make a depiction of architectural, spatial, topographical factors combined in a sublime incident? Can a methodology be defined to photograph these structures? How can photographs be made of large-scale architecture that cannot be seen or experienced in their entirety? The meaning of the term sublime has become diluted in contemporary usage, often being used inaccurately in description of something exquisite or delightful. This project revisits 18th-century formulations of this aesthetic categorisation, alongside historical travel literature, representations of landscape in painting and photography and contemporary architectural and photographic discourses. These references enable a thorough understanding of principles of aesthetic composition, resulting in the creation of a new understanding of the sublime and methodology for photographing large-scale motorway architecture. Employing a photographic aesthetic that embraces representation and post-production enhancement of Fine Art practice, the project culminates in the production of 29 photographs that form a narrative series exploring incidents of the sublime within motorway architecture between the Alps and Naples

SCOT - Spatial Clustering Of German Towns

The GIS revolution and the increasing availability of GIS databases emphasize the need to better understand the typically large amounts of spatial data. Clustering is a fundamental task in Spatial Data Mining and many contributions from researchers in the field of Knowledge Discovery are proposing solutions for class identification in spatial databases. The term spatial data refers to a collection of (similar) spatial objects, e.g. areas, lines or points. In addition to geographic information, each object also possesses non-spatial attributes. In order to apply traditional data mining algorithms to such data, the spatial structure ans relational properties must be made explicite. SCOT deals with the special case of grouping German towns. The towns are related to each other by the various streets connecting them. Each town also possesses an inner spatial structure, the local street network, and further non-spatial information. This thesis considers all three kinds of information for the clustering of towns. It exploits the concept of neighborhood to capture relational constraints, measures the similarity of the structures of local street networks and transforms the most important non-spatial attributes. SCOT is part of a project at Fraunhofer IAIS, Germany, and has been successfully applied in practice

Physics based supervised and unsupervised learning of graph structure

Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss\u27s law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

The North Sky and the Otherworld: Journeys of the Dead in the Neolithic Considered

The majority of Irish passage tombs (c. 230) predominantly date to the Middle Neolithic (c. 3600–3000 BC). A small number of summit cairns may also contain passage tombs because of their round form, proximity and intervisibility. The island’s passage tombs and related cairns share distinguishing characteristics – elevated siting, visibility and long-range views of distant horizons in varying directions of the compass. This chapter presents the findings of the first scenic analysis of the horizon and views at these sites recorded at an island scale. The method uses measured orientations of horizon sectors related to observed variation in horizon range. This shows that tomb location was likely selected with a preference for view of the distant horizon and, curiously, also in the northerly direction in many cases. This sector of the horizon lies beyond the extreme rising and setting limits of the sun and moon. It is also the region of circumpolar stars which never rise or set and are perpetual in a viewing sense. The hypothesis that the process of cremation released the spirit of the dead to travel to the abode of the ancestors in the north sky, a zone commonly associated with death and the afterlife by other later cultures, is explored