Multidimensional Network Analysis: Models, Analytics, Applications
نویسنده
چکیده
Multidimensional networks (also known as multilayer, multiplex, or multislice) are receiving increased attention from computer scientists, physicists, mathematicians, and other scientists, thanks also to many application scenarios and increased availability of data for analytics. Social sciences are interested in the multidimensional aspects of the connections between individuals, not only by looking at different types of connections (i.e., different relationships that may co-exist at the same time), but also taking into account different dimensions like space, time, and text, all at the same time. Economists are also looking at modelling and analysing hidden or explicit relationships between companies, products, CEOs, and all the other entities within the financial environment. Due to the higher complexity of the relationships that this type of networks is able to capture, and the higher expressiveness of the models to represent them, Multidimensional Network Analysis is both a technically challenging field, and a powerful means to extract knowledge from real world phenomena. During this talk, we will review the basic aspects of Multidimensional Network Analysis, like history, models, structural analysis, some focused (open) problems like community detection or link prediction, and some application scenarios like public safety and financial analytics. Copyright c © 2015 by the paper’s authors. Copying permitted only for private and academic purposes. In: M. Atzmueller, F. Lemmerich (Eds.): Proceedings of 6th International Workshop on Mining Ubiquitous and Social Environments (MUSE), co-located with the ECML PKDD 2015. Published at http://ceur-ws.org
منابع مشابه
Application of Big Data Analytics in Power Distribution Network
Smart grid enhances optimization in generation, distribution and consumption of the electricity by integrating information and communication technologies into the grid. Today, utilities are moving towards smart grid applications, most common one being deployment of smart meters in advanced metering infrastructure, and the first technical challenge they face is the huge volume of data generated ...
متن کاملPlanning-Based Reasoning for Automated Large-Scale Data Analysis
In this paper, we apply planning-based reasoning to orchestrate the data analysis process automatically, with a focus on two applications: early detection of health complications in critical care, and detection of anomalous behaviors of network hosts in enterprise networks. Our system uses expert knowledge and AI planning to reason about possibly incomplete, noisy, or inconsistent observations,...
متن کاملNetwork Analytics ER Model - Towards a Conceptual View of Network Analytics
This paper proposes a conceptual modelling paradigm for network analysis applications, called the Network Analytics ER model (NAER). Not only data requirements but also query requirements are captured by the conceptual description of network analysis applications. This unified analytical framework allows us to flexibly build a number of topology schemas on the basis of the underlying core schem...
متن کاملGuest Editorial: Special Section on Visual Analytics
VISUAL analytics is the science of analytical reasoning supported by highly interactive visual interfaces. People use visual analytics tools and techniques to synthesize information; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessments effectively...
متن کاملEra of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions
Many problems in computational neuroscience, neuroinformatics, pattern/image recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide often a natural and compact representation for such massive multidimensional data via suitable low-rank approximations. Big data a...
متن کامل