CenGCN : centralized convolutional networks with vertex imbalance for scale-free graphs
- Xia, Feng, Wang, Lei, Tang, Tao, Chen, Xin, Kong, Xiangjie, Oatley, Giles, King, Irwin
- Authors: Xia, Feng , Wang, Lei , Tang, Tao , Chen, Xin , Kong, Xiangjie , Oatley, Giles , King, Irwin
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 35, no. 5 (2023), p. 4555-4569
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- Description: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE.
- Authors: Xia, Feng , Wang, Lei , Tang, Tao , Chen, Xin , Kong, Xiangjie , Oatley, Giles , King, Irwin
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 35, no. 5 (2023), p. 4555-4569
- Full Text:
- Reviewed:
- Description: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE.
Lost at starting line : predicting maladaptation of university freshmen based on educational big data
- Guo, Teng, Bai, Xiaomei, Zhen, Shihao, Abid, Shagufta, Xia, Feng
- Authors: Guo, Teng , Bai, Xiaomei , Zhen, Shihao , Abid, Shagufta , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of the Association for Information Science and Technology Vol. 74, no. 1 (2023), p. 17-32
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- Description: The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of “at-risk” students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods. © 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.
- Authors: Guo, Teng , Bai, Xiaomei , Zhen, Shihao , Abid, Shagufta , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of the Association for Information Science and Technology Vol. 74, no. 1 (2023), p. 17-32
- Full Text:
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- Description: The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of “at-risk” students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods. © 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.
COVID-19 datasets : a brief overview
- Sun, Ke, Li, Wuyang, Saikrishna, Vidya, Chadhar, Mehmood, Xia, Feng
- Authors: Sun, Ke , Li, Wuyang , Saikrishna, Vidya , Chadhar, Mehmood , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Science and Information Systems Vol. 19, no. 3 (2022), p. 1115-1132
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- Description: The outbreak of the COVID-19 pandemic affects lives and social-economic development around the world. The affecting of the pandemic has motivated researchers from different domains to find effective solutions to diagnose, prevent, and estimate the pandemic and relieve its adverse effects. Numerous COVID-19 datasets are built from these studies and are available to the public. These datasets can be used for disease diagnosis and case prediction, speeding up solving problems caused by the pandemic. To meet the needs of researchers to understand various COVID-19 datasets, we examine and provide an overview of them. We organise the majority of these datasets into three categories based on the category of ap-plications, i.e., time-series, knowledge base, and media-based datasets. Organising COVID-19 datasets into appropriate categories can help researchers hold their focus on methodology rather than the datasets. In addition, applications and COVID-19 datasets suffer from a series of problems, such as privacy and quality. We discuss these issues as well as potentials of COVID-19 datasets. © 2022, ComSIS Consortium. All rights reserved.
- Authors: Sun, Ke , Li, Wuyang , Saikrishna, Vidya , Chadhar, Mehmood , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Science and Information Systems Vol. 19, no. 3 (2022), p. 1115-1132
- Full Text:
- Reviewed:
- Description: The outbreak of the COVID-19 pandemic affects lives and social-economic development around the world. The affecting of the pandemic has motivated researchers from different domains to find effective solutions to diagnose, prevent, and estimate the pandemic and relieve its adverse effects. Numerous COVID-19 datasets are built from these studies and are available to the public. These datasets can be used for disease diagnosis and case prediction, speeding up solving problems caused by the pandemic. To meet the needs of researchers to understand various COVID-19 datasets, we examine and provide an overview of them. We organise the majority of these datasets into three categories based on the category of ap-plications, i.e., time-series, knowledge base, and media-based datasets. Organising COVID-19 datasets into appropriate categories can help researchers hold their focus on methodology rather than the datasets. In addition, applications and COVID-19 datasets suffer from a series of problems, such as privacy and quality. We discuss these issues as well as potentials of COVID-19 datasets. © 2022, ComSIS Consortium. All rights reserved.
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.
MESH : a flexible manifold-embedded semantic hashing for cross-modal retrieval
- Zhong, Fangming, Wang, Guangze, Chen, Zhikui, Xia, Feng
- Authors: Zhong, Fangming , Wang, Guangze , Chen, Zhikui , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147569-147579
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- Description: Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 2013 IEEE.
- Authors: Zhong, Fangming , Wang, Guangze , Chen, Zhikui , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147569-147579
- Full Text:
- Reviewed:
- Description: Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 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:
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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.
- 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.
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