Big networks: A survey

Abstract

A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called big network. Big networks are generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further.

Graph Learning: A Survey

“…Centralized algorithms cannot handle this since the computational complexity of these algorithms would significantly increase with the growth of vertex number. The design of distributed algorithms for dealing with big networks is a critical problem yet to be solved [20]. One major benefit of distributed algorithms is that the algorithms can be executed in multiple CPUs or GPUs simultaneously, and hence the running time can be reduced significantly.…”

Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

“…The structural complexity also increases with the requirements of representing more information within large-scale networks and the difficulty of processing networks with sparse edge information. Structural complexity of large-scale networks with thousands and millions of nodes results from a complicated and higher-order inner structure [117], which are common in DTs like city IoT [111] and DT of manufacturing with big data [118]. Structural complexity of sparse networks, given fewer edges, lies in their restrictions of attributes processing [105], [119] and optimal modelling [120].…”

“…It is hard to find studies on CNS built and modelled with real time information because of its observability and the difficulties of building realistic real time data simulator. Though there are studies on CNS built with big data [117], it is still hard to achieve data efficiency at a "real-time" level. Though in some applications of CNS in a DT like IoT, where networked information can be gathered in a real time by sensors and integrated into a Knowledge Graph or a block chain, such a method is not applicable in all application scenarios given the requirement of equipment.…”

Surf or sleep? Understanding the influence of bedtime patterns on campus

“…A data-driven framework for sleep habit detection and analysis that can serve the daily management of universities is urgently needed. During the past decade, we have witnessed an increased utilization of electronic devices like smartphones and tablets [7]. As they become more lightweight, people may easily use these devices even in bed.…”