Big Networks: A Survey

TL;DR

This survey explores the concept of big networks, discussing their structural characteristics, analysis methods, models, and applications, highlighting challenges and future research directions in large-scale network science.

Contribution

It introduces the concept of big networks, provides a comprehensive framework for their analysis, and reviews state-of-the-art models, methods, and applications in large-scale network analysis.

Findings

Big networks have complex multi-level structures.

Various models and algorithms are effective for big network analysis.

Applications include community detection, link prediction, and recommendation.

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.