Acar Tamersoy, Bo Xie, Stephen L. Lenkey, Bryan R. Routledge, Duen Horng Chau, S. Navathe
How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., CEO vs. CFO)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission (SEC). We analyzed 12 million transactions by 370 thousand insiders spanning 1986 to 2012, the largest reported in academia. We explore the temporal and network-centric aspects of the trading behaviors of insiders, and make surprising and counter-intuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the transaction types, the company sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades and enable them to adapt their detection strategies towards these dynamics.
{"title":"Inside insider trading: Patterns & discoveries from a large scale exploratory analysis","authors":"Acar Tamersoy, Bo Xie, Stephen L. Lenkey, Bryan R. Routledge, Duen Horng Chau, S. Navathe","doi":"10.1145/2492517.2500288","DOIUrl":"https://doi.org/10.1145/2492517.2500288","url":null,"abstract":"How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., CEO vs. CFO)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission (SEC). We analyzed 12 million transactions by 370 thousand insiders spanning 1986 to 2012, the largest reported in academia. We explore the temporal and network-centric aspects of the trading behaviors of insiders, and make surprising and counter-intuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the transaction types, the company sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades and enable them to adapt their detection strategies towards these dynamics.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134369687","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}
A. Albano, Jean-Loup Guillaume, Sebastien Heymann, B. L. Grand
Diffusion phenomena occur in many kinds of real-world complex networks, e.g., biological, information or social networks. Because of this diversity, several types of diffusion models have been proposed in the literature: epidemiological models, threshold models, innovation adoption models, among others. Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks, and much remains to be done to understand diffusion on evolving networks. In order to study the impact of graph dynamics on diffusion, we propose in this paper an innovative approach based on a notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to isolate somehow the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on synthetic graphs, as well as on a dataset extracted from the Github sofware sharing platform.
{"title":"A matter of time - intrinsic or extrinsic - for diffusion in evolving complex networks","authors":"A. Albano, Jean-Loup Guillaume, Sebastien Heymann, B. L. Grand","doi":"10.1145/2492517.2492634","DOIUrl":"https://doi.org/10.1145/2492517.2492634","url":null,"abstract":"Diffusion phenomena occur in many kinds of real-world complex networks, e.g., biological, information or social networks. Because of this diversity, several types of diffusion models have been proposed in the literature: epidemiological models, threshold models, innovation adoption models, among others. Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks, and much remains to be done to understand diffusion on evolving networks. In order to study the impact of graph dynamics on diffusion, we propose in this paper an innovative approach based on a notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to isolate somehow the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on synthetic graphs, as well as on a dataset extracted from the Github sofware sharing platform.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129567883","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 the context of a Regional Platform of Innovation for future tourism, we define a model and a decision support system for semantic analysis of social networks embedding networks of opinions, aiming at representing and understanding territorial uses coming out from digital marks. The territorial and touristic observatory we develop behind a digital platform is designed to complete usual tools of economic intelligence, helping in governance and in the identification of touristic products and services of tomorrow, so as to foster the rise of territorial economy. In the early stage of the project, the contributions we present are (1) a social semantic graph structure, (2) the definition of semantic degree centrality, (3) a process and definitions for interfacing semantic networks of opinions with social semantic networks of uses, and (4) a first use case experimentation. Public deployment is planned for 2013 and economical impacts will be measured on next years.
{"title":"Opinion mining and semantic analysis of touristic social networks","authors":"Christophe Thovex, F. Trichet","doi":"10.1145/2492517.2500235","DOIUrl":"https://doi.org/10.1145/2492517.2500235","url":null,"abstract":"In the context of a Regional Platform of Innovation for future tourism, we define a model and a decision support system for semantic analysis of social networks embedding networks of opinions, aiming at representing and understanding territorial uses coming out from digital marks. The territorial and touristic observatory we develop behind a digital platform is designed to complete usual tools of economic intelligence, helping in governance and in the identification of touristic products and services of tomorrow, so as to foster the rise of territorial economy. In the early stage of the project, the contributions we present are (1) a social semantic graph structure, (2) the definition of semantic degree centrality, (3) a process and definitions for interfacing semantic networks of opinions with social semantic networks of uses, and (4) a first use case experimentation. Public deployment is planned for 2013 and economical impacts will be measured on next years.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132394586","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}
Renewable energy research has recently been seen as one of the most important areas of studies by budding doctoral researchers. The paper is an attempt to study the trends of renewable energy research on the basis of PhD dissertations database provided by University Grants Commission (Government of India). The database provides the information about researcher, guide, title, university/Department and year. On the bases of the PhD database a unique combination of researcher and guide was established by searching scientometric output of the combination on SCOPUS database. The results were analyzed using Pajek social network diagrams and UCINET and further network between Researcher, Guide, Institution and future research collaborations were illustratively mapped. The final output highlights the Universities' contribution towards creation of renewable technology research not only by creating the human resource but also by various modes of networking, be it researcher to guide, university to university, researcher to research or a combination of all. It also shed light on the behavior of a researcher after completing of doctoral programme.
{"title":"Researcher-guide networking: A case of renewable energy research","authors":"Vipan Kumar, R. Sagar, S. Narula","doi":"10.1145/2492517.2500294","DOIUrl":"https://doi.org/10.1145/2492517.2500294","url":null,"abstract":"Renewable energy research has recently been seen as one of the most important areas of studies by budding doctoral researchers. The paper is an attempt to study the trends of renewable energy research on the basis of PhD dissertations database provided by University Grants Commission (Government of India). The database provides the information about researcher, guide, title, university/Department and year. On the bases of the PhD database a unique combination of researcher and guide was established by searching scientometric output of the combination on SCOPUS database. The results were analyzed using Pajek social network diagrams and UCINET and further network between Researcher, Guide, Institution and future research collaborations were illustratively mapped. The final output highlights the Universities' contribution towards creation of renewable technology research not only by creating the human resource but also by various modes of networking, be it researcher to guide, university to university, researcher to research or a combination of all. It also shed light on the behavior of a researcher after completing of doctoral programme.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133095727","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}
Social Networks promote information sharing between people everywhere and at all times. Mining data produced in this data-rich environment can be extremely useful. Frequent itemset mining plays an important role in mining associations, correlations, sequential patterns, causality, episodes, multidimensional patterns, max-patterns, partial periodicity, emerging patterns, and many other significant data mining tasks in social networks. With the exponential growth of social network data towards a terabyte or more, most of the traditional frequent itemset mining algorithms become ineffective due to either huge resource requirements or large communications overhead. Cloud computing has proved that processing very large datasets over commodity clusters can be done by providing the right programming model. As a parallel programming model, MapReduce, one of most important techniques for cloud computing, has emerged in the mining of datasets of terabyte scale or larger on clusters of computers. In this paper, we propose an efficient frequent itemset mining algorithm, called IMRApriori, based on MapReduce framework which deals with Hadoop cloud, a parallel store and computing platform. The paper demonstrates experimental results to corroborate the theoretical claims.
{"title":"Efficient mining of frequent itemsets in social network data based on MapReduce framework","authors":"Zahra Farzanyar, N. Cercone","doi":"10.1145/2492517.2500301","DOIUrl":"https://doi.org/10.1145/2492517.2500301","url":null,"abstract":"Social Networks promote information sharing between people everywhere and at all times. Mining data produced in this data-rich environment can be extremely useful. Frequent itemset mining plays an important role in mining associations, correlations, sequential patterns, causality, episodes, multidimensional patterns, max-patterns, partial periodicity, emerging patterns, and many other significant data mining tasks in social networks. With the exponential growth of social network data towards a terabyte or more, most of the traditional frequent itemset mining algorithms become ineffective due to either huge resource requirements or large communications overhead. Cloud computing has proved that processing very large datasets over commodity clusters can be done by providing the right programming model. As a parallel programming model, MapReduce, one of most important techniques for cloud computing, has emerged in the mining of datasets of terabyte scale or larger on clusters of computers. In this paper, we propose an efficient frequent itemset mining algorithm, called IMRApriori, based on MapReduce framework which deals with Hadoop cloud, a parallel store and computing platform. The paper demonstrates experimental results to corroborate the theoretical claims.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132687681","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}
Models and techniques for the extraction and analysis of communities from social network data have become a major area of research. Most of the prominent approaches exploit the link structure among users based on, e.g., information about followers or the exchange of messages among users. However, there are also other types of information that are useful for extracting communities from social network data, such as geographic information associated with postings and users. In this paper, we present a novel approach to discover so-called regional communities. Motivated by the fact that more and more postings to social networks include the geo-location of users, we claim that communities also form even if their users do not necessarily interact but are posting (similar) messages in both spatial and temporal proximity. To discover such regional communities we propose a generative probabilistic model based on spatial latent Dirichlet allocation (SLDA) that unveils not only regional communities but also topics associated with these communities. We demonstrate the effectiveness of our approach using Twitter data and compare the properties of communities detected that way with communities discovered by approaches using link graphs.
{"title":"A spatial LDA model for discovering regional communities","authors":"T. V. Canh, Michael Gertz","doi":"10.1145/2492517.2492616","DOIUrl":"https://doi.org/10.1145/2492517.2492616","url":null,"abstract":"Models and techniques for the extraction and analysis of communities from social network data have become a major area of research. Most of the prominent approaches exploit the link structure among users based on, e.g., information about followers or the exchange of messages among users. However, there are also other types of information that are useful for extracting communities from social network data, such as geographic information associated with postings and users. In this paper, we present a novel approach to discover so-called regional communities. Motivated by the fact that more and more postings to social networks include the geo-location of users, we claim that communities also form even if their users do not necessarily interact but are posting (similar) messages in both spatial and temporal proximity. To discover such regional communities we propose a generative probabilistic model based on spatial latent Dirichlet allocation (SLDA) that unveils not only regional communities but also topics associated with these communities. We demonstrate the effectiveness of our approach using Twitter data and compare the properties of communities detected that way with communities discovered by approaches using link graphs.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115017611","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}
Complex networks have attracted much research attentions. Community detection is an important problem in complex network which is useful in a variety of applications such as information propagation, link prediction, recommendations and marketing. In this paper, we focus on discovering overlapping community structure using link partition. We proposed a LDA-based link partition (LBLP) method which can find communities with adjustable range of overlapping. This method employs topic model to detect link partition, which can calculate the community belonging factor for each link. Based on the belonging factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of our solution on both real-world and synthesized networks. The experiment results demonstrate that the approach can find meaningful and relevant link community structure.
{"title":"Topic model-based link community detection with adjustable range of overlapping","authors":"Le Yu, Bin Wu, Bai Wang","doi":"10.1145/2492517.2492581","DOIUrl":"https://doi.org/10.1145/2492517.2492581","url":null,"abstract":"Complex networks have attracted much research attentions. Community detection is an important problem in complex network which is useful in a variety of applications such as information propagation, link prediction, recommendations and marketing. In this paper, we focus on discovering overlapping community structure using link partition. We proposed a LDA-based link partition (LBLP) method which can find communities with adjustable range of overlapping. This method employs topic model to detect link partition, which can calculate the community belonging factor for each link. Based on the belonging factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of our solution on both real-world and synthesized networks. The experiment results demonstrate that the approach can find meaningful and relevant link community structure.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"8 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132238233","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 presents findings from a study of the email network of a large scientific research organization, focusing on methods for visualizing and modeling organizational hierarchies within large, complex network datasets. In the first part of the paper, we find that visualization and interpretation of complex organizational network data is facilitated by integration of network data with information on formal organizational divisions and levels. By aggregating and visualizing email traffic between organizational units at various levels, we derive several insights into how large subdivisions of the organization interact with each other and with outside organizations. In the second part of the paper, we propose a power law model for predicting degree distribution of organizational email traffic based on hierarchical relationships between managers and employees. This model considers the influence of global email announcements sent from managers to all employees under their supervision, and the role support staff play in generating email traffic, acting as agents for managers.
{"title":"Visualization and modeling of structural features of a large organizational email network","authors":"B. Sims, N. Sinitsyn, S. Eidenbenz","doi":"10.1145/2492517.2492642","DOIUrl":"https://doi.org/10.1145/2492517.2492642","url":null,"abstract":"This paper presents findings from a study of the email network of a large scientific research organization, focusing on methods for visualizing and modeling organizational hierarchies within large, complex network datasets. In the first part of the paper, we find that visualization and interpretation of complex organizational network data is facilitated by integration of network data with information on formal organizational divisions and levels. By aggregating and visualizing email traffic between organizational units at various levels, we derive several insights into how large subdivisions of the organization interact with each other and with outside organizations. In the second part of the paper, we propose a power law model for predicting degree distribution of organizational email traffic based on hierarchical relationships between managers and employees. This model considers the influence of global email announcements sent from managers to all employees under their supervision, and the role support staff play in generating email traffic, acting as agents for managers.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133775874","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}
Viral marketing, which uses the “word of mouth” marketing technique over virtual networks, relies on the selection of a small subset of most influential users in the network for efficient marketing. Nonetheless, most existing viral marketing techniques ignore the dynamic nature of the virtual network. In this paper, we develop a novel framework that exploits the temporal dynamics of the network to select an optimal subset of users that maximize the marketing influence over the network.
{"title":"Maximizing influence of viral marketing via evolutionary user selection","authors":"Sanket Anil Naik, Qi Yu","doi":"10.1145/2492517.2492580","DOIUrl":"https://doi.org/10.1145/2492517.2492580","url":null,"abstract":"Viral marketing, which uses the “word of mouth” marketing technique over virtual networks, relies on the selection of a small subset of most influential users in the network for efficient marketing. Nonetheless, most existing viral marketing techniques ignore the dynamic nature of the virtual network. In this paper, we develop a novel framework that exploits the temporal dynamics of the network to select an optimal subset of users that maximize the marketing influence over the network.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133573162","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}
Microblogging disseminates realtime information through dynamic user interactions. While it is intuitive that such interactions may generate patterns, it is difficult to identify and characterize them in satisfactory detail. In this paper, we propose using a combination of dynamic graphs and time-series to study the dynamics and collective behaviors in microblogging. To enable automatic pattern identification, a distance metric is developed to incorporate the heterogeneous aspects of the dynamical interactions. We demonstrate the effectiveness of the proposed approach using a month long Twitter dataset and show that the new representation and distance metric are both essential for discovering the patterns of collective microblogging, such as propagation of breaking news, advertisement, social movement, and interest group formation.
{"title":"Identifying dynamics and collective behaviors in microblogging traces","authors":"Huan-Kai Peng, R. Marculescu","doi":"10.1145/2492517.2500250","DOIUrl":"https://doi.org/10.1145/2492517.2500250","url":null,"abstract":"Microblogging disseminates realtime information through dynamic user interactions. While it is intuitive that such interactions may generate patterns, it is difficult to identify and characterize them in satisfactory detail. In this paper, we propose using a combination of dynamic graphs and time-series to study the dynamics and collective behaviors in microblogging. To enable automatic pattern identification, a distance metric is developed to incorporate the heterogeneous aspects of the dynamical interactions. We demonstrate the effectiveness of the proposed approach using a month long Twitter dataset and show that the new representation and distance metric are both essential for discovering the patterns of collective microblogging, such as propagation of breaking news, advertisement, social movement, and interest group formation.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133378727","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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