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Big networks : a survey

- Bedru, Hayat, Yu, Shuo, Xiao, Xinru, Zhang, Da, Xia, Feng

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

- Yu, Shuo, Feng, Yufan, Zhang, Da, Bedru, Hayat, Xu, Bo, Xia, Feng

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.
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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.

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Network embedding : taxonomies, frameworks and applications

- Hou, Mingliang, Ren, Jing, Zhang, Da, Kong, Xiangjie, Zhang, Dongyu, Xia, Feng

Authors: Hou, Mingliang , Ren, Jing , Zhang, Da , Kong, Xiangjie , Zhang, Dongyu , Xia, Feng
Date: 2020
Type: Text , Journal article , Review
Relation: Computer Science Review Vol. 38, no. (2020), p.
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Description: Networks are a general language for describing complex systems of interacting entities. In the real world, a network always contains massive nodes, edges and additional complex information which leads to high complexity in computing and analyzing tasks. Network embedding aims at transforming one network into a low dimensional vector space which benefits the downstream network analysis tasks. In this survey, we provide a systematic overview of network embedding techniques in addressing challenges appearing in networks. We first introduce concepts and challenges in network embedding. Afterwards, we categorize network embedding methods using three categories, including static homogeneous network embedding methods, static heterogeneous network embedding methods and dynamic network embedding methods. Next, we summarize the datasets and evaluation tasks commonly used in network embedding. Finally, we discuss several future directions in this field. © 2020 Elsevier Inc.

Ranking station importance with human mobility patterns using subway network datasets

- Xia, Feng, Wang, Jinzhong, Kong, Xiangjie, Zhang, Da, Wang, Zhibo

Authors: Xia, Feng , Wang, Jinzhong , Kong, Xiangjie , Zhang, Da , Wang, Zhibo
Date: 2020
Type: Text , Journal article
Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 21, no. 7 (2020), p. 2840-2852
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Description: Complex networks have become an active interdisciplinary field of research inspired by the empirical study of various networks. A subway network is a real-world example of complex networks in the transportation domain, which has attracted growing attention in network analysis recently. Analyzing human mobility patterns, specifically in ranking subway stations closely bounded by urban subway planning and individuals' travel experience, is still an open issue. In this paper, we propose a novel ranking method of station importance (SIRank) by utilizing human mobility patterns and improved PageRank algorithm. Specifically, by analyzing human mobility patterns of the subway system in Shanghai, we demonstrate both static and dynamic characteristics using two network models (Shanghai subway static network and Shanghai subway passenger network). In particular, the SIRank focuses on bi-directional passenger flow analysis between origins and destinations to iteratively generate the importance value for each station. We implement a range of the experiments to illustrate the effectiveness of SIRank using the real-world subway transaction datasets. The results demonstrate that the hit ratio in SIRank reaches 60% in the top five stations, which is much higher than that of ranking by a weighted mixed index (WMIRank) and ranking by node degree (NDRank) approaches. © 2000-2011 IEEE.

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The gene of scientific success

- Kong, Xiangjie, Zhang, Jun, Zhang, Da, Bu, Yi, Ding, Ying, Xia, Feng

Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
Date: 2020
Type: Text , Journal article
Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
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Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.

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