Deep Learning for Spatio-Temporal Data Mining: a Survey Senzhang Wang, Jiannong Cao, Fellow, IEEE, Philip S
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From Static to Temporal Network Theory: Applications to Functional Brain Connectivity
METHODS From static to temporal network theory: Applications to functional brain connectivity William Hedley Thompson1, Per Brantefors1, and Peter Fransson1 1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden Keywords: Resting-state, Temporal network theory, Temporal networks, Functional connectome, Dynamic functional connectivity Downloaded from http://direct.mit.edu/netn/article-pdf/1/2/69/1091947/netn_a_00011.pdf by guest on 28 September 2021 ABSTRACT an open access journal Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python Citation: Thompson, W. H., Brantefors, package Teneto. P., & Fransson, P. (2017). From static to temporal network theory: Applications to functional brain connectivity. -
Are the Different Layers of a Social Network Conveying the Same
Manivannan et al. REGULAR ARTICLE Are the different layers of a social network conveying the same information? Ajaykumar Manivannan1, W. Quin Yow1, Roland Bouffanais1y and Alain Barrat2,3*y *Correspondence: [email protected] Abstract 1Singapore University of Technology and Design, 8 Comprehensive and quantitative investigations of social theories and phenomena Somapah Road, 487372, Singapore increasingly benefit from the vast breadth of data describing human social 2 Aix Marseille Univ, Universit´ede relations, which is now available within the realm of computational social science. Toulon, CNRS, CPT, Marseille, France Such data are, however, typically proxies for one of the many interaction layers 3Data Science Laboratory, composing social networks, which can be defined in many ways and are typically Institute for Scientific Interchange composed of communication of various types (e.g., phone calls, face-to-face (ISI) Foundation, Torino, Italy Full list of author information is communication, etc.). As a result, many studies focus on one single layer, available at the end of the article corresponding to the data at hand. Several studies have, however, shown that y Equal contributor these layers are not interchangeable, despite the presence of a certain level of correlations between them. Here, we investigate whether different layers of interactions among individuals lead to similar conclusions with respect to the presence of homophily patterns in a population|homophily represents one of the widest studied phenomenon in social networks. To this aim, we consider a dataset describing interactions and links of various nature in a population of Asian students with diverse nationalities, first language and gender. -
Urban Anomaly Analytics: Description, Detection, and Prediction
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2019 1 Urban Anomaly Analytics: Description, Detection, and Prediction Mingyang Zhang, Tong Li,Student Member, IEEE, Yue Yu, Yong Li,Senior Member, IEEE, Pan Hui,Fellow, IEEE and Yu Zheng,Senior Member, IEEE, Abstract—Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed. Index Terms—Anomaly detection, spatiotemporal data mining, urban computing, event detection. F 1 INTRODUCTION their travel routes or transport. In this way, it will save RBAN anomalies are typically unusual events occur- people a lot of time on the commute and further improve U ring in urban environments, such as traffic conges- their quality of lives. tion and unexpected crowd gathering, which may pose Meanwhile, smart devices and various kinds of sensors tremendous threats to public safety and stability if not widely located in cities collect the data produced in urban timely handled [1], [2]. -
Temporal Network Metrics and Their Application to Real World Networks
Temporal network metrics and their application to real world networks John Kit Tang Robinson College University of Cambridge 2011 This dissertation is submitted for the degree of Doctor of Philosophy Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation does not exceed the regulation length of 60 000 words, including tables and footnotes. Summary The analysis of real social, biological and technological networks has attracted a lot of attention as technological advances have given us a wealth of empirical data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture. This motivates the work in this dissertation: new tools for temporal complex network analysis are required when analysing real networks that inherently change over time. Firstly, we introduce the temporal graph model and formalise the notion of shortest temporal paths, used extensively in graph theory, and show that as static graphs ignore the time order of contacts, the available links are overestimated and the true shortest paths are underestimated. In addition, contrary to intuition, we find that slowly evolving graphs can be efficient for information dissemination due to small-world behaviour in temporal graphs. -
Machine Learning for Urban Computing Bilgeçağ Aydoğdu, Albert Ali Salah1
Machine Learning for Urban Computing Bilgeçağ Aydoğdu, Albert Ali Salah1 1. Introduction Increasing urbanization rates and growth of population in urban areas have brought complex problems to be solved in various fields such as urban planning, energy, health, safety, and transportation. Urban computing (UC) is the name of the process that involves collection, integration and analysis of heterogeneous urban data coming from various types of sensors in the city, with the purpose of tackling urban problems (Zheng et al., 2014). Within this framework, the first step of UC is to collect data through sensors like mobile phones, surveillance cameras, vehicles with sensors, or social media applications. The second step is to store, clean, and index the spatiotemporal data through different data management techniques by preparing it for data analytics. The third step is to apply different analysis approaches to solve urban tasks related to different problems. Machine learning methods are some of the most powerful analysis approaches for this purpose, because they can help model arbitrarily complex relationships between measurements and target variables, and they can adapt to dynamically changing environments, which is very important in UC, as cities are constantly in motion. Machine learning (ML) is a subfield of artificial intelligence (AI) that combines computer science and statistics with the objective of optimizing a performance criterion by learning models from data or from past experience (Alpaydın, 2020). The capabilities of ML have sharply increased in the last decades due to increased abilities of computers to store and process large volumes of data, as well as due to theoretical advances. -
ICT of the New Wave of Computing for Sustainable Urban Forms: Their Big Data and Context–Aware Augmented Typologies and Design Concepts
Accepted Manuscript Title: ICT of the New Wave of Computing for Sustainable Urban Forms: Their Big Data and Context–Aware Augmented Typologies and Design Concepts Author: Simon Elias Bibri John Krogstie PII: S2210-6707(16)30247-5 DOI: http://dx.doi.org/doi:10.1016/j.scs.2017.04.012 Reference: SCS 637 To appear in: Received date: 14-8-2016 Revised date: 20-4-2017 Accepted date: 20-4-2017 Please cite this article as: Bibri, S. E., and Krogstie, J.,ICT of the New Wave of Computing for Sustainable Urban Forms: Their Big Data and ContextndashAware Augmented Typologies and Design Concepts, Sustainable Cities and Society (2017), http://dx.doi.org/10.1016/j.scs.2017.04.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ICT of the New Wave of Computing for Sustainable Urban Forms: Their Big Data and Context–Aware Augmented Typologies and Design Concepts Simon Elias Bibri 1 NTNU Norwegian University of Science and Technology, Department of Computer and Information Science and Department of Urban Planning and Design, Sem Saelands veie 9, NO–7491, Trondheim, Norway John Krogstie NTNU Norwegian University of Science and Technology, Department of Computer and Information Science, Sem Saelands veie 9, NO–7491, Trondheim, Norway E–mail address: [email protected] Abstract Undoubtedly, sustainable development has inspired a generation of scholars and practitioners in different disciplines into a quest for the immense opportunities created by the development of sustainable urban forms for human settlements that will enable built environments to function in a more constructive and efficient way. -
Temporal Network Approach to Explore Bike Sharing Usage Patterns
Temporal Network Approach to Explore Bike Sharing Usage Patterns Aizhan Tlebaldinova1, Aliya Nugumanova1, Yerzhan Baiburin1, Zheniskul Zhantassova2, Markhaba Karmenova2 and Andrey Ivanov3 1Laboratory of Digital Technologies and Modeling, S. Amanzholov East Kazakhstan State University, Shakarim Street, Ust-Kamenogorsk, Kazakhstan 2Department of Computer Modeling and Information Technologies, S. Amanzholov East Kazakhstan State University, Shakarim Street, Ust-Kamenogorsk, Kazakhstan 3JSC «Bipek Auto» Kazakhstan, Bazhov Street, Ust-Kamenogorsk, Kazakhstan Keywords: Bike Sharing, Temporal Network, Betweenness Centrality, Clustering, Time Series. Abstract: The bike-sharing systems have been attracting increase research attention due to their great potential in developing smart and green cities. On the other hand, the mathematical aspects of their design and operation generate a lot of interesting challenges for researchers in the field of modeling, optimization and data mining. The mathematical apparatus that can be used to study bike sharing systems is not limited only to optimization methods, space-time analysis or predictive analytics. In this paper, we use temporal network methodology to identify stable trends and patterns in the operation of the bike sharing system using one of the largest bike-sharing framework CitiBike NYC as an example. 1 INTRODUCTION cluster centralization measurements for one hour of a certain day of the week into one value and decode The bike-sharing systems have been attracting it into a color cell. In order to make heat map more increase research attention due to their great contrast and effective, we propose an unusual way of potential in developing smart and green cities. On collapsing data, in which only the highest cluster the other hand, the mathematical aspects of their centralization values are taken into account. -
Machine Learning Approaches to Bike-Sharing Systems: a Systematic Literature Review
International Journal of Geo-Information Review Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review Vitória Albuquerque 1,* , Miguel Sales Dias 1,2 and Fernando Bacao 1 1 NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal; [email protected] (M.S.D.); [email protected] (F.B.) 2 Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal * Correspondence: [email protected] Abstract: Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction. Keywords: bike-sharing systems; machine learning; classification; prediction; PRISMA method 1. Introduction Citation: Albuquerque, V.; Sales Changes are taking place in the future development of the transport sector. To this Dias, M.; Bacao, F. Machine Learning aim, concrete plans are already in place, such as the Sustainable Development Goals Approaches to Bike-Sharing Systems: (SDGs) [1], the New Urban Agenda [2] and the Organisation for Economic Co-operation A Systematic Literature Review. -
Nonlinearity + Networks: a 2020 Vision
Nonlinearity + Networks: A 2020 Vision Mason A. Porter Abstract I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include “higher-order” interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators. 1 Introduction Network analysis is one of the most exciting areas of applied and industrial math- ematics [121, 141, 143]. It is at the forefront of numerous and diverse applications throughout the sciences, engineering, technology, and the humanities. The study of networks combines tools from numerous areas of mathematics, including graph theory, linear algebra, probability, statistics, optimization, statistical mechanics, sci- entific computation, and nonlinear dynamics. In this chapter, I give a short overview of popular and state-of-the-art topics in network science. My discussions of these topics, which I draw preferentially from ones that relate to nonlinear and complex systems, will be terse, but I will cite many review articles and highlight specific research papers for those who seek more details. This chapter is not a review or even a survey; instead, I give my perspective on the short-term and medium-term future of network analysis in applied mathematics for 2020 and beyond. -
Understanding Urban Dynamics Via Context-Aware Tensor Factorization with Neighboring Regularization
1 Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization Jingyuan Wang, Junjie Wu, Ze Wang, Fei Gao, and Zhang Xiong Abstract—Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations. Index Terms—Urban Dynamics, Tensor Factorizations, Urban Planning, Spatio-Temporal Pattern, GPS Trajectory F 1 INTRODUCTION As reported by the World Bank1, at the end of 2016 understand the evolving rules of cities so as to make proper more than 53% population of the world, i.e., about 3.7 urban planning. -
Smart Futures Meet Northern Realities: Anthropological Perspectives on the Design and Adoption of Urban Computing
B 126 OULU 2015 B 126 UNIVERSITY OF OULU P.O. Box 8000 FI-90014 UNIVERSITY OF OULU FINLAND ACTA UNIVERSITATIS OULUENSIS ACTA UNIVERSITATIS OULUENSIS ACTA HUMANIORAB Johanna Ylipulli Johanna Ylipulli Professor Esa Hohtola SMART FUTURES MEET University Lecturer Santeri Palviainen NORTHERN REALITIES: Postdoctoral research fellow Sanna Taskila ANTHROPOLOGICAL PERSPECTIVES ON THE Professor Olli Vuolteenaho DESIGN AND ADOPTION University Lecturer Veli-Matti Ulvinen OF URBAN COMPUTING Director Sinikka Eskelinen Professor Jari Juga University Lecturer Anu Soikkeli Professor Olli Vuolteenaho UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF HUMANITIES, Publications Editor Kirsti Nurkkala CULTURAL ANTHROPOLOGY; FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING, ISBN 978-952-62-0747-6 (Paperback) DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING ISBN 978-952-62-0748-3 (PDF) ISSN 0355-3205 (Print) ISSN 1796-2218 (Online) ACTA UNIVERSITATIS OULUENSIS B Humaniora 126 JOHANNA YLIPULLI SMART FUTURES MEET NORTHERN REALITIES: ANTHROPOLOGICAL PERSPECTIVES ON THE DESIGN AND ADOPTION OF URBAN COMPUTING Academic dissertation to be presented with the assent of the Doctoral Training Committee of Human Sciences of the University of Oulu for public defence in Kuusamonsali (YB210), Linnanmaa, on 6 March 2015, at 1 p.m. UNIVERSITY OF OULU, OULU 2015 Copyright © 2015 Acta Univ. Oul. B 126, 2015 Supervised by Professor Hannu I. Heikkinen Professor Jaakko Suominen Professor Timo Ojala Reviewed by Professor Sarah Pink Doctor Anne Galloway Opponent Professor Gertraud Koch ISBN 978-952-62-0747-6 (Paperback) ISBN 978-952-62-0748-3 (PDF) ISSN 0355-3205 (Printed) ISSN 1796-2218 (Online) Cover Design Raimo Ahonen JUVENES PRINT TAMPERE 2015 Ylipulli, Johanna, Smart futures meet northern realities: Anthropological perspectives on the design and adoption of urban computing. -
Dyhcn: Dynamic Hypergraph Convolutional Networks
Under review as a conference paper at ICLR 2021 DYHCN: DYNAMIC HYPERGRAPH CONVOLUTIONAL NETWORKS Anonymous authors Paper under double-blind review ABSTRACT Hypergraph Convolutional Network (HCN) has become a default choice for cap- turing high-order relations among nodes, i.e., encoding the structure of a hyper- graph. However, existing HCN models ignore the dynamic evolution of hyper- graphs in the real-world scenarios, i.e., nodes and hyperedges in a hypergraph change dynamically over time. To capture the evolution of high-order relations and facilitate relevant analytic tasks, we formulate dynamic hypergraph and devise the Dynamic Hypergraph Convolutional Networks (DyHCN). In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations. The HC is delicately designed by equipping inner attention and outer at- tention, which adaptively aggregate nodes’ features to hyperedge and estimate the importance of each hyperedge connected to the centroid node, respectively. Exten- sive experiments on the Tiigo and Stocktwits datasets show that DyHCN achieves superior performance over existing methods, which implies the effectiveness of capturing the property of dynamic hypergraphs by HC and TE modules. 1 INTRODUCTION Graph Convolutional Network (GCN) Scarselli et al. (2008) extends deep neural networks to process graph data, which encodes the relations between nodes via propagating node features over the graph structure. GCN has become a promising solution in a wide spectral of graph analytic tasks, such as relation detection Schlichtkrull et al. (2018) and recommendation Ying et al. (2018). An emer- gent direction of GCN research is extending the graph covolution operations to hypergraphs, i.e., hypergraph convolutional networks Zhu et al.