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.
- 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.
- Full Text:
- Reviewed:
- 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.
Motifs in big networks : methods and applications
- Yu, Shuo, Xu, Jin, Zhang, Chen, Xia, Feng, Almakhadmeh, Zafer, Tolba, Amr
- Authors: Yu, Shuo , Xu, Jin , Zhang, Chen , Xia, Feng , Almakhadmeh, Zafer , Tolba, Amr
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 183322-183338
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- Description: Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed. © 2013 IEEE.
- Authors: Yu, Shuo , Xu, Jin , Zhang, Chen , Xia, Feng , Almakhadmeh, Zafer , Tolba, Amr
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 183322-183338
- Full Text:
- Reviewed:
- Description: Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed. © 2013 IEEE.
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.
- Full Text:
- Reviewed:
- 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.
- 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.
- Full Text:
- Reviewed:
- 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.
TOSNet : a topic-based optimal subnetwork identification in academic networks
- Bedru, Hayat, Zhao, Wenhong, Alrashoud, Mubarak, Tolba, Amr, Guo, He, Xia, Feng
- Authors: Bedru, Hayat , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Guo, He , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 201015-201027
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- Description: Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Authors: Bedru, Hayat , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Guo, He , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 201015-201027
- Full Text:
- Reviewed:
- Description: Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
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