DEFINE: friendship detection based on node enhancement
- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng
- Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
- Full Text:
- Reviewed:
- Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
- Description: E1
- Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
- Full Text:
- Reviewed:
- Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
- Description: E1
ANSWER : generating information dissemination network on campus
- Qing, Qing, Guo, Teng, Zhang, Dongyu, Xia, Feng
- Authors: Qing, Qing , Guo, Teng , Zhang, Dongyu , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
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- Description: Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.
- Authors: Qing, Qing , Guo, Teng , Zhang, Dongyu , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
- Full Text:
- Reviewed:
- Description: Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.
Data-driven decision-making in COVID-19 response : a survey
- Yu, Shuo, Qing, Qing, Zhang, Chen, Shehzad, Ahsan, Oatley, Giles, Xia, Feng
- Authors: Yu, Shuo , Qing, Qing , Zhang, Chen , Shehzad, Ahsan , Oatley, Giles , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 4 (2021), p. 989-1002
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- Description: COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE.
- Authors: Yu, Shuo , Qing, Qing , Zhang, Chen , Shehzad, Ahsan , Oatley, Giles , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 4 (2021), p. 989-1002
- Full Text:
- Reviewed:
- Description: COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE.
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