The previous studies on detection of communities on complex networks were focused on nondirected graphs, such as Neural Networks, social networks, social interrelations, the contagion of diseases, and bibliographies. ...The previous studies on detection of communities on complex networks were focused on nondirected graphs, such as Neural Networks, social networks, social interrelations, the contagion of diseases, and bibliographies. However, there are also other problems whose modeling entails obtaining a weakly connected directed graph such as the student access to the university, the public transport networks, or trophic chains. Those cases deserve particularized study with an analysis and the resolution adjusted to them. Additionally, this is a challenge, since the existing algorithms in most of the cases were originally designed for non-directed graphs or symmetrical and regular graphs. Our proposal is a Benchmark Generator of Weakly Connected Directed Graphs whose properties can be defined by the end-users according to their necessities. The source code of the generators described in this article is available in GitHub under the GNU license.展开更多
financial services:for example,GPS and Bluetooth inspire location-based services,and search and web technologies motivate online shopping,reviews,and payments.These business services have become more connected than ev...financial services:for example,GPS and Bluetooth inspire location-based services,and search and web technologies motivate online shopping,reviews,and payments.These business services have become more connected than ever,and as a result,financial frauds have become a significant challenge.Therefore,combating financial risks in the big data era requires breaking the borders of traditional data,algorithms,and systems.An increasing number of studies have addressed these challenges and proposed new methods for risk detection,assessment,and forecasting.As a key contribution,we categorize these works in a rational framework:first,we identify the data that can be used to identify risks.We then discuss how big data can be combined with the emerging tools to effectively learn or analyze financial risk.Finally,we highlight the effectiveness of these methods in real-world applications.Furthermore,we stress on the importance of utilizing multi-channel information,graphs,and networks of long-range dependence for the effective identification of financial risks.We conclude our survey with a discussion on the new challenges faced by the financial sector,namely,deep fake technology,adversaries,causal and interpretable inference,privacy protection,and microsimulations.展开更多
基金supported by the Project“Complex Networks”from the Instituto Universitario de Matematica Multidisciplinar(IUMM)of the Universitat Politecnica de Valencia(UPV)[under Grant number(266500194)20170251-Complex-Networks-UPV]
文摘The previous studies on detection of communities on complex networks were focused on nondirected graphs, such as Neural Networks, social networks, social interrelations, the contagion of diseases, and bibliographies. However, there are also other problems whose modeling entails obtaining a weakly connected directed graph such as the student access to the university, the public transport networks, or trophic chains. Those cases deserve particularized study with an analysis and the resolution adjusted to them. Additionally, this is a challenge, since the existing algorithms in most of the cases were originally designed for non-directed graphs or symmetrical and regular graphs. Our proposal is a Benchmark Generator of Weakly Connected Directed Graphs whose properties can be defined by the end-users according to their necessities. The source code of the generators described in this article is available in GitHub under the GNU license.
基金supported by the National Natural Science Foundation of China under Grant Nos.91746301,61772498,61802370,and 61902380.
文摘financial services:for example,GPS and Bluetooth inspire location-based services,and search and web technologies motivate online shopping,reviews,and payments.These business services have become more connected than ever,and as a result,financial frauds have become a significant challenge.Therefore,combating financial risks in the big data era requires breaking the borders of traditional data,algorithms,and systems.An increasing number of studies have addressed these challenges and proposed new methods for risk detection,assessment,and forecasting.As a key contribution,we categorize these works in a rational framework:first,we identify the data that can be used to identify risks.We then discuss how big data can be combined with the emerging tools to effectively learn or analyze financial risk.Finally,we highlight the effectiveness of these methods in real-world applications.Furthermore,we stress on the importance of utilizing multi-channel information,graphs,and networks of long-range dependence for the effective identification of financial risks.We conclude our survey with a discussion on the new challenges faced by the financial sector,namely,deep fake technology,adversaries,causal and interpretable inference,privacy protection,and microsimulations.
