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The aim of this workshop called Advances in Mining Large-Scale Time Dependent Graphs (TD-LSG) is to bring together active scholars and practitioners of dynamic graphs. Graph models and algorithms are ubiquitous of a large number of application domains, ranging from transportation to social networks, semantic web, or data mining. However, many applications require graph models that are time dependent. For example, applications related to urban mobility analysis employ a graph structure of the underlying road network. Indeed, the nature of such networks are spatiotemporal. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. So, we call time-dependent graphs, the graphs that have this spatiotemporal feature.

Important dates:

  • Paper Submission Deadline: June 11, 2018 (Extended)
  • Author Notification: June 30, 2018
  • Camera Ready Deadline: July 13, 2018
  • Workshop: August 31, 2018

Useful links:

Keynote 1


Wagner Meira Jr., Professor, Universidade Federal de Minas Gerais, Brazil


Title: Scalability and Efficiency in Graph Mining
Abstract:

Despite significant research, graph mining remains a challenging task, due to characteristics such as its computational complexity and the large spectrum of models that may be mined. In this talk we discuss some of these challenges and focus on strategies targeted at two significant issues in relevant scenarios, such as social networks and bioinformatics. The first issue is scalability and we present some strategies not only for creating computationally scalable solutions, but also for developing them more easily. The second issue is the efficiency of the patterns mined, and we present new graph mining models as well as robust sampling strategies for them. We conclude by summarizing the lessons learned and presenting current trends.


Bio:

Wagner Meira Jr. obtained his PhD from the University of Rochester in 1997 and is Full Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. He has published more than 200 papers in top venues and is co-author of the book Data Mining and Analysis - Fundamental Concepts and Algorithms published by Cambridge University Press in 2014. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, cybersecurity, bioinformatics, and e-governance.



Keynote 2


Tamer Ozsu, Professor, Cheriton School of Computer Science University of Waterloo


Title: An Overview of Graph Analytics Platforms
Abstract:

Graph data are of growing importance in many applications, including the semantic web (i.e., RDF), social network analysis, bioinformatics, software engineering, e-commerce, finance and trading, fraud detection, and recommendation systems, because they model complicated structures and relationships well. The size and complexity of these graphs raise significant data management and data analysis challenges. This has led to a number of different algorithms and approaches to graph processing as well as systems that are based on these algorithms. In this presentation, I will focus on the platforms that have been developed to facilitate graph analytics. I will start with a classification of various approaches and systems, and then discuss the systems that have been developed to facilitate graph analytics.


Bio:

M. Tamer Özsu is a University Professor in the David R. Cheriton School of Computer Science of the University of Waterloo. His research is in data management focusing on large-scale data distribution and management of non-traditional data. He is a Fellow of the Royal Society of Canada, American Association for Advancement of Research (AAAS), the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronics Engineers (IEEE). He is an elected member of the Science Academy of Turkey, and member of Sigma Xi.