Thomas Marcoux, Oluwaseyi Adeliyi, Dr Nidhi Agarwal
YouTube is the second most popular website on the internet and a major actor in information propagation, therefore making it efficient as a potential vehicle of misinformation. Current tools available for video platforms tend to hyperfocus on metadata aggregation and neglect the analysis of the actual videos. In an attempt to provide analysts the tools they need to perform various research (behavioral, political analysis, sociology,etc.), we present VTracker (formerly YouTubeTracker), an online analytical tool. Some of the insight analysts can derive from this tool are inorganic behavior detection and algorithmic manipulation. We aim to make the analysis of YouTube content and user behavior accessible not only to information scientists but also communication researchers, journalists, sociologists, and many more. We demonstrate the utility of the tool through some real world data samples.
{"title":"Characterizing video-based online information environment using VTracker","authors":"Thomas Marcoux, Oluwaseyi Adeliyi, Dr Nidhi Agarwal","doi":"10.1145/3487351.3489480","DOIUrl":"https://doi.org/10.1145/3487351.3489480","url":null,"abstract":"YouTube is the second most popular website on the internet and a major actor in information propagation, therefore making it efficient as a potential vehicle of misinformation. Current tools available for video platforms tend to hyperfocus on metadata aggregation and neglect the analysis of the actual videos. In an attempt to provide analysts the tools they need to perform various research (behavioral, political analysis, sociology,etc.), we present VTracker (formerly YouTubeTracker), an online analytical tool. Some of the insight analysts can derive from this tool are inorganic behavior detection and algorithmic manipulation. We aim to make the analysis of YouTube content and user behavior accessible not only to information scientists but also communication researchers, journalists, sociologists, and many more. We demonstrate the utility of the tool through some real world data samples.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129514009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
k nearest neighbor (kNN) query is an essential graph data management tool to find relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response, the graph indexing methods are one of the promising approaches. However, they struggle to handle large-scale complex networks since constructing indexes and to querying kNN nodes in the large-scale networks are computationally expensive. In this paper, we propose a novel graph indexing algorithm for a fast kNN query on large networks. To overcome the aforementioned limitations, our algorithm generates two types of indexes based on the topological properties of complex networks. Our extensive experiments on real-world graphs clarify that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than the state-of-the-art methods.
{"title":"Fast indexing algorithm for efficient kNN queries on complex networks","authors":"Suomi Kobayashi, Shohei Matsugu, Hiroaki Shiokawa","doi":"10.1145/3487351.3489442","DOIUrl":"https://doi.org/10.1145/3487351.3489442","url":null,"abstract":"k nearest neighbor (kNN) query is an essential graph data management tool to find relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response, the graph indexing methods are one of the promising approaches. However, they struggle to handle large-scale complex networks since constructing indexes and to querying kNN nodes in the large-scale networks are computationally expensive. In this paper, we propose a novel graph indexing algorithm for a fast kNN query on large networks. To overcome the aforementioned limitations, our algorithm generates two types of indexes based on the topological properties of complex networks. Our extensive experiments on real-world graphs clarify that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than the state-of-the-art methods.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128397462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mounir Haddad, Cécile Bothorel, P. Lenca, Dominique Bedart
Graph autoencoders (GAE), also known as graph embedding methods, learn latent representations of the nodes of a graph in a low-dimensional space where the structural information is preserved. While real-world graphs are generally dynamic, only a few embedding methods handle the temporal dimension: Even though they have proven their reliability, the majority of the embedding techniques address the case of static networks and present poor performances when applied to temporal ones. In this paper, we present a generic method to temporalize static graph autoencoders, i.e. adapt different static graph embedding methods to the case of temporal networks. This is made possible by learning optimal connections between timesteps' graphs in order to form a single merged spatio-temporal network. We prove that this highly improves the inference tasks' accuracy of the temporalized methods. We also show that the learned connections are directly related to nodes characteristics and can be used beyond the scope of the embedding they are designed for.
{"title":"Temporalizing static graph autoencoders to handle temporal networks","authors":"Mounir Haddad, Cécile Bothorel, P. Lenca, Dominique Bedart","doi":"10.1145/3487351.3488333","DOIUrl":"https://doi.org/10.1145/3487351.3488333","url":null,"abstract":"Graph autoencoders (GAE), also known as graph embedding methods, learn latent representations of the nodes of a graph in a low-dimensional space where the structural information is preserved. While real-world graphs are generally dynamic, only a few embedding methods handle the temporal dimension: Even though they have proven their reliability, the majority of the embedding techniques address the case of static networks and present poor performances when applied to temporal ones. In this paper, we present a generic method to temporalize static graph autoencoders, i.e. adapt different static graph embedding methods to the case of temporal networks. This is made possible by learning optimal connections between timesteps' graphs in order to form a single merged spatio-temporal network. We prove that this highly improves the inference tasks' accuracy of the temporalized methods. We also show that the learned connections are directly related to nodes characteristics and can be used beyond the scope of the embedding they are designed for.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116374973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In many online social networks (OSNs), a limited portion of profiles emerges and reaches a large base of followers, i.e., the so-called social influencers. One of their main goals is to increase their fanbase to increase their visibility, engaging users through their content. In this work, we propose a novel parallel between the ecosystem of OSNs and the stock exchange market. Followers act as private investors, and they follow influencers, i.e., buy stocks, based on their individual preferences and on the information they gather through external sources. In this preliminary study, we show how the approaches proposed in the context of the stock exchange market can be successfully applied to social networks. Our case study focuses on 60 Italian Instagram influencers and shows how their followers short-term trends obtained through Bollinger bands become close to those found in external sources, Google Trends in our case, similarly to phenomena already observed in the financial market. Besides providing a strong correlation between these different trends, our results pose the basis for studying social networks with a new lens, linking them with a different domain.
{"title":"The stock exchange of influencers: a financial approach for studying fanbase variation trends","authors":"Fabio Bertone, L. Vassio, Martino Trevisan","doi":"10.1145/3487351.3488413","DOIUrl":"https://doi.org/10.1145/3487351.3488413","url":null,"abstract":"In many online social networks (OSNs), a limited portion of profiles emerges and reaches a large base of followers, i.e., the so-called social influencers. One of their main goals is to increase their fanbase to increase their visibility, engaging users through their content. In this work, we propose a novel parallel between the ecosystem of OSNs and the stock exchange market. Followers act as private investors, and they follow influencers, i.e., buy stocks, based on their individual preferences and on the information they gather through external sources. In this preliminary study, we show how the approaches proposed in the context of the stock exchange market can be successfully applied to social networks. Our case study focuses on 60 Italian Instagram influencers and shows how their followers short-term trends obtained through Bollinger bands become close to those found in external sources, Google Trends in our case, similarly to phenomena already observed in the financial market. Besides providing a strong correlation between these different trends, our results pose the basis for studying social networks with a new lens, linking them with a different domain.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose the FwLTR and BwLTR, two new centrality measures based on the Linear Threshold model. In contrast to the Linear Threshold rank (LTR), these measures differentiate between the incoming and the outgoing neighborhoods of the activation set that initiates the spreading process. Their rankings are distinguishable from the rest of the centrality measures considered traditionally. However, LTR and BwLTR behave quite similarly, while FwLTR is clearly different.
{"title":"Forward and backward linear threshold ranks","authors":"M. Blesa, Pau García-Rodríguez, M. Serna","doi":"10.1145/3487351.3488355","DOIUrl":"https://doi.org/10.1145/3487351.3488355","url":null,"abstract":"We propose the FwLTR and BwLTR, two new centrality measures based on the Linear Threshold model. In contrast to the Linear Threshold rank (LTR), these measures differentiate between the incoming and the outgoing neighborhoods of the activation set that initiates the spreading process. Their rankings are distinguishable from the rest of the centrality measures considered traditionally. However, LTR and BwLTR behave quite similarly, while FwLTR is clearly different.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper models, for the first time, the homelessness system as a network of interconnected services which individuals traverse over time towards securing stable housing, and formalizes the concept of stability upon exit of the system. A computational analysis of individual-level longitudinal homelessness data shows that the ultimate goal is either reached quickly or not at all, regardless of starting conditions, indicating the importance of addressing the homeless' needs early on.
{"title":"Peeking through the homelessness system with a network science lens","authors":"C. Chelmis, Khandker Sadia Rahman","doi":"10.1145/3487351.3488321","DOIUrl":"https://doi.org/10.1145/3487351.3488321","url":null,"abstract":"This paper models, for the first time, the homelessness system as a network of interconnected services which individuals traverse over time towards securing stable housing, and formalizes the concept of stability upon exit of the system. A computational analysis of individual-level longitudinal homelessness data shows that the ultimate goal is either reached quickly or not at all, regardless of starting conditions, indicating the importance of addressing the homeless' needs early on.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132552006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, social networking is popular. As such, numerous social networking sites (e.g., Facebook, YouTube, Instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data, and is awaiting to be analyzed and mined via social network analysis and mining. In general, social networks can be represented as graphs. Because of the dynamic nature of social networking, edges and/or vertices keep adding to (or deleting from) the graphs. We present in this paper a mathematical model for friend discovery from dynamic social graphs. In particular, we focus on both linear algebra and graph theory approaches to discover interesting social entities---such as active followers---from dynamic social networks represented as dynamic directional social graphs.
{"title":"A mathematical model for friend discovery from dynamic social graphs","authors":"C. Leung, S. Singh","doi":"10.1145/3487351.3489473","DOIUrl":"https://doi.org/10.1145/3487351.3489473","url":null,"abstract":"Nowadays, social networking is popular. As such, numerous social networking sites (e.g., Facebook, YouTube, Instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data, and is awaiting to be analyzed and mined via social network analysis and mining. In general, social networks can be represented as graphs. Because of the dynamic nature of social networking, edges and/or vertices keep adding to (or deleting from) the graphs. We present in this paper a mathematical model for friend discovery from dynamic social graphs. In particular, we focus on both linear algebra and graph theory approaches to discover interesting social entities---such as active followers---from dynamic social networks represented as dynamic directional social graphs.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134068087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenhe Pan, Shuang Jiang, Juntao Su, Muzhe Guo, Yuanlin Zhang
Since the first cased of COVID-19 was identified in December 2019, a plethora of different drugs have been tested for COVID-19 treatment, making it a daunting task to keep track of the rapid growth of COVID-19 research landscape. Using the existing scientific literature search systems to develop a deeper understanding of COVID-19 related clinical experiments and results turns to be increasingly complicated. In this paper, we build a named entity recognition-based framework to extract information accurately and generate knowledge graph efficiently from a myriad of clinical test results articles. Of the tested drugs to treat COVID-19, we also develop a question answering system answers to medical questions regarding COVID-19 related symptoms using Wikipedia articles. We combine the state-of-the-art question answering model - Bidirectional Encoder Representations from Transformers (BERT), with Knowledge Graph to answer patients' questions about treatment options for their symptoms. This generated knowledge graph is user-friendly with intuitive and convenient tools to find the supporting and/or contradictory references of certain drugs with properties such as side effects, target population, etc. The trained question answering platform provides a straightforward and error-tolerant way to query for treatment suggestions given uses' input symptoms.
{"title":"Knowledge graph based platform of COVID-19 drugs and symptoms","authors":"Zhenhe Pan, Shuang Jiang, Juntao Su, Muzhe Guo, Yuanlin Zhang","doi":"10.1145/3487351.3489484","DOIUrl":"https://doi.org/10.1145/3487351.3489484","url":null,"abstract":"Since the first cased of COVID-19 was identified in December 2019, a plethora of different drugs have been tested for COVID-19 treatment, making it a daunting task to keep track of the rapid growth of COVID-19 research landscape. Using the existing scientific literature search systems to develop a deeper understanding of COVID-19 related clinical experiments and results turns to be increasingly complicated. In this paper, we build a named entity recognition-based framework to extract information accurately and generate knowledge graph efficiently from a myriad of clinical test results articles. Of the tested drugs to treat COVID-19, we also develop a question answering system answers to medical questions regarding COVID-19 related symptoms using Wikipedia articles. We combine the state-of-the-art question answering model - Bidirectional Encoder Representations from Transformers (BERT), with Knowledge Graph to answer patients' questions about treatment options for their symptoms. This generated knowledge graph is user-friendly with intuitive and convenient tools to find the supporting and/or contradictory references of certain drugs with properties such as side effects, target population, etc. The trained question answering platform provides a straightforward and error-tolerant way to query for treatment suggestions given uses' input symptoms.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many cyber security experts, organizations, and cyber criminals are active users on online social networks (OSNs). Therefore, detecting cyber security related accounts on OSNs and monitoring their activities can be very useful for different purposes such as cyber threat intelligence, detecting and preventing cyber attacks and online harms on OSNs, and evaluating the effectiveness of cyber security awareness activities on OSNs. In this paper, we report our work on developing several machine learning based classifiers for detecting cyber security related accounts on Twitter, including a base-line classifier for detecting cyber security related accounts in general, and three sub-classifiers for detecting three subsets of cyber security related accounts (individuals, hackers, and academia). To train and test the classifiers, we followed a more systemic approach (based on a cyber security taxonomy, real-time sampling of tweets, and crowdsourcing) to construct a dataset of cyber security related accounts with multiple tags assigned to each account. For each classifier, we considered a richer set of features than those used in past studies. Among five machine learning models tested, the Random Forest model achieved the best performance: 93% for the baseline classifier, 88-91% for the three sub-classifiers. We also studied feature reduction of the base-line classifier and showed that using just six features we can already achieve the same performance.
{"title":"Detecting cyber security related Twitter accounts and different sub-groups: a multi-classifier approach","authors":"Mohamad Imad Mahaini, Shujun Li","doi":"10.1145/3487351.3492716","DOIUrl":"https://doi.org/10.1145/3487351.3492716","url":null,"abstract":"Many cyber security experts, organizations, and cyber criminals are active users on online social networks (OSNs). Therefore, detecting cyber security related accounts on OSNs and monitoring their activities can be very useful for different purposes such as cyber threat intelligence, detecting and preventing cyber attacks and online harms on OSNs, and evaluating the effectiveness of cyber security awareness activities on OSNs. In this paper, we report our work on developing several machine learning based classifiers for detecting cyber security related accounts on Twitter, including a base-line classifier for detecting cyber security related accounts in general, and three sub-classifiers for detecting three subsets of cyber security related accounts (individuals, hackers, and academia). To train and test the classifiers, we followed a more systemic approach (based on a cyber security taxonomy, real-time sampling of tweets, and crowdsourcing) to construct a dataset of cyber security related accounts with multiple tags assigned to each account. For each classifier, we considered a richer set of features than those used in past studies. Among five machine learning models tested, the Random Forest model achieved the best performance: 93% for the baseline classifier, 88-91% for the three sub-classifiers. We also studied feature reduction of the base-line classifier and showed that using just six features we can already achieve the same performance.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Control of complex networks is one of the most challenging open problems within network science. One view says that we can only claim to fully understand a network if we have the ability to influence or control it and predict the results of the employed control mechanisms. The area of control and controllability has progressed notably in the past ten years with several frameworks proposed namely, structural, exact, and physical. With continuing advancement in the area, the need to develop effective and efficient control methods that provide robust control is increasingly critical. The ultimate responsibility for controlling the network lies with the set of driver nodes that, according to the classical definition of the control theory of complex systems, can steer the network from any given state to a desired final state. To be able to develop better control mechanisms, we need to understand the relationship between different network structures and the number of driver nodes needed to control a given structure. This will allow understanding of which networks might be easier to control and the resources needed to control them. In this paper, we present a systematic study that builds an understanding of how network profiles (random (R), small-world (SW), scale-free (SF)) influence the number of driver nodes needed for control. Additionally, we also consider real social networks and identify their driver nodes set to further expand the discussion. We mean to find a correlation between network structure measures and number of driver nodes. Our results show that there is in fact a strong relationship between these.
{"title":"An insight into network structure measures and number of driver nodes","authors":"Abida Sadaf, Luke Mathieson, Katarzyna Musial","doi":"10.1145/3487351.3488557","DOIUrl":"https://doi.org/10.1145/3487351.3488557","url":null,"abstract":"Control of complex networks is one of the most challenging open problems within network science. One view says that we can only claim to fully understand a network if we have the ability to influence or control it and predict the results of the employed control mechanisms. The area of control and controllability has progressed notably in the past ten years with several frameworks proposed namely, structural, exact, and physical. With continuing advancement in the area, the need to develop effective and efficient control methods that provide robust control is increasingly critical. The ultimate responsibility for controlling the network lies with the set of driver nodes that, according to the classical definition of the control theory of complex systems, can steer the network from any given state to a desired final state. To be able to develop better control mechanisms, we need to understand the relationship between different network structures and the number of driver nodes needed to control a given structure. This will allow understanding of which networks might be easier to control and the resources needed to control them. In this paper, we present a systematic study that builds an understanding of how network profiles (random (R), small-world (SW), scale-free (SF)) influence the number of driver nodes needed for control. Additionally, we also consider real social networks and identify their driver nodes set to further expand the discussion. We mean to find a correlation between network structure measures and number of driver nodes. Our results show that there is in fact a strong relationship between these.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"402 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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