Big networks : a survey
- Authors: Bedru, Hayat , Yu, Shuo , Xiao, Xinru , Zhang, Da , Xia, Feng
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Science Review Vol. 37, no. (2020), p.
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- Description: 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 a big network. A big networks is 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. © 2020 Elsevier Inc.
Motif discovery in networks : a survey
- Authors: Yu, Shuo , Feng, Yufan , Zhang, Da , Bedru, Hayat , Xu, Bo , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Science Review Vol. 37, no. (2020), p.
- Full Text: false
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- Description: Motifs are regarded as network blocks because motifs can be used to present fundamental patterns in networks. Motif discovery is well applied in various scientific problems, including subgraph mining and graph isomorphism tasks. This paper analyzes and summarizes current motif discovery algorithms in the field of network science with both efficiency and accuracy perspectives. In this paper, we present motif discovery algorithms, including MFinder, FanMod, Grochow, MODA, Kavosh, G-tries, QuateXelero, color-coding approaches, and GPU-based approaches. Based on that, we discuss the real-world applications of the algorithms mentioned above under different scenarios. Since motif discovery algorithms are diffusely demanded in many applications, several challenges may be firstly handled, including high computational complexity, higher order motif discovery, same motif detection, discovering heterogeneous sizes of motifs, as well as motif discovery results visualization. This work sheds light on current research progress and future research orientations. © 2020 Elsevier Inc.