The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from soci...The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.展开更多
Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis. As the complete information of network is often difficult to obtain, such as net...Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis. As the complete information of network is often difficult to obtain, such as networks of web pages, research papers and Facebook users, people can only detect community structure from a certain source vertex with limited knowledge of the entire graph. The existing approaches do well in measuring the community quality, but they are largely dependent on source vertex and putting too strict policy in agglomerating new vertices. Moreover, they have predefined parameters which are difficult to obtain. This paper proposes a method to find local community structure by analyzing link similarity between the community and the vertex. Inspired by the fact that elements in the same community are more likely to share common links, we explore community structure heuristically by giving priority to vertices which have a high link similarity with the community. A three-phase process is also used for the sake of improving quality of community structure. Experimental results prove that our method performs effectively not only in computer-generated graphs but also in real-world graphs.展开更多
The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals ...The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals been readily available for analysis. Two popular topics in the study of social networks are community detection and sentiment analysis. Community detection seeks to find groups of associated individuals within networks, and sentiment analysis attempts to determine how individuals are feeling. While these are generally treated as separate issues, this study takes an integrative approach and uses community detection output to enable community-level sentiment analysis. Community detection is performed using the Walktrap algorithm on a network of Twitter users associated with Microsoft Corporation’s @technet account. This Twitter account is one of several used by Microsoft Corporation primarily for communicating with information technology professionals. Once community detection is finished, sentiment in the tweets produced by each of the communities detected in this network is analyzed based on word sentiment scores from the well-known SentiWordNet lexicon. The combination of sentiment analysis with community detection permits multilevel exploration of sentiment information within the @technet network, and demonstrates the power of combining these two techniques.展开更多
Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have...Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very important.In addition,the similarity among nodes inside the same cluster is greater than among nodes from other clusters.Lately,the global and local methods of community detection have been getting more attention.Therefore,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information.Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation.This process is conducted with the same color till nodes reach maximum similarity.Finally,the communities are formed,and a clear community graph is displayed to the user.Our proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,etc.We perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art models.The results prove that our model is efficient in all aspects,because it quickly identifies communities in the network.Moreover,it can easily be used for friendship recommendations or in business recommendation systems.展开更多
Community based churn prediction,or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries.While churn predi...Community based churn prediction,or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries.While churn prediction until recent times have focused only on transactional dataset(targeted approach),the untargeted approach through product advisement,digital marketing and expressions in customer’s opinion on the social media like Twitter,have not been fully harnessed.Although this data source has become an important influencing factor with lasting impact on churn management.Since Social Network Analysis(SNA)has become a blended approach for churn prediction and management in modern era,customers residing online predominantly and collectively decide and determines the momentum of churn prediction,retention and decision support.In existing SNA approaches,customers are classified as churner or non-churner(1 or 0).Oftentimes,the customer’s opinion is also neglected and the network structure of community members are not exploited.Consequently,the pattern and influential abilities of customers’opinion on relative members of the community are not analysed.Thus,the research developed a Churn Service Information Graph(CSIG)to define a quadruple churn category(churner,potential churner,inertia customer,premium customer)for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA.The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group.Consequently,every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition,retention and/or decision supports in churn management.展开更多
Sciencenet.cn is the leading online portal serving the Chinese scientific community. This paper intends to analyze the interdisciplinary and intradisciplinary knowledge communication patterns based on friends-list lin...Sciencenet.cn is the leading online portal serving the Chinese scientific community. This paper intends to analyze the interdisciplinary and intradisciplinary knowledge communication patterns based on friends-list links in the blog community at Sciencenet.cn by using hyperlink analysis and social network analysis. The major findings are: 1) More bloggers have an academic background in management science and life science; 2) there are some core actors in co-inlink network and co-outlink network, who take the lead in engaging with knowledge exchange activities and produce a great influence on interdisciplinary communication; 3) interactive relationships commonly exist between a blogger and those on his/her friends list, and the most linked-to blogs usually play a key role in generating interactive communication; 4) management science has the highest co-inlink count with life science or information science and it has the highest co-outlink count with life science or mathematical and physical science; 5) management science and life science have the greatest impact on information science and the interdisciplinary knowledge communication will also produce relatively significant influence on the development of information science discipline. It is our hope that this research can serve as a reference source for the future studies of academic virtual communities, and the development of mechanisms for facilitating increased engagement in knowledge exchange activities in academic virtual communities.展开更多
With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in physics sociology, computer society, etc...With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in physics sociology, computer society, etc. Although this investigation of community structures has motivated many diverse algorithms, most of them are unsuitable when dealing with large networks due to their computational cost. In this paper, we present a faster algorithm ComTector which is more efficient for the community detection in large complex networks based on the nature of overlapping cliques. This algorithm does not require any priori knowledge about the number or the original division of the communities. With respect to practical applications, ComTector is challenging with five different types of networks including the classic Zachary Karate Club, Scientific Collaboration Network South Florida Free Word Association Network, Urban Traffic Network North America Power Grid and the Telecommunication Call Network. Experimental results show that our algorithm can discover meaningful communities that meet both the objective basis and our intuitions.展开更多
In the field of social network analysis,Link Predic-tion is one of the hottest topics which has been attracted attentions in academia and industry.So far,literatures for solving link prediction can be roughly divided ...In the field of social network analysis,Link Predic-tion is one of the hottest topics which has been attracted attentions in academia and industry.So far,literatures for solving link prediction can be roughly divided into two categories:similarity-based and learning-based methods.The learning-based methods have higher accuracy,but their time complexities are too high for complex networks.However,the similarity-based methods have the advantage of low time consumption,so improving their accuracy becomes a key issue.In this paper,we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm(SSDBA),In SSDBA,we first detect communities of a social network and identify active nodes based on community average threshold(CAT)and node average threshold(NAT)in each community.Second,we propose the stretch shrink distance(SSD)model to iteratively calculate the changes of distances between active nodes and their local neighbors.Finally,we make predictions when these links'distances tend to converge.Furthermore,extensive parameters learning have been carried out in experiments.We compare our SSDBA with other popular approaches.Experimental results validate the effectiveness and efficiency of proposed algorithm.展开更多
Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out us...Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out usingassociation rule mining or community detection approach. This article usesboth methods to investigate a transaction dataset collected from a brick-andmortargrocery store. The findings reveal interesting purchasing patterns oflocal residents and prompt us to consider dynamic modeling of the productnetwork in the future.展开更多
文摘The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.
基金supported by the National Natural Science Foundation of China under Grant No.61170193the Doctoral Program of the Ministry of Education of China under Grant No.20090172120035+2 种基金the Natural Science Foundation of Guangdong Province of China under Grant No.S2012010010613the Fundamental Research Funds for the Central Universities of South China University of Technology of China under Grant No.2012ZM0087the Pearl River Science & Technology Start Project of China under Grant No. 2012J2200007
文摘Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis. As the complete information of network is often difficult to obtain, such as networks of web pages, research papers and Facebook users, people can only detect community structure from a certain source vertex with limited knowledge of the entire graph. The existing approaches do well in measuring the community quality, but they are largely dependent on source vertex and putting too strict policy in agglomerating new vertices. Moreover, they have predefined parameters which are difficult to obtain. This paper proposes a method to find local community structure by analyzing link similarity between the community and the vertex. Inspired by the fact that elements in the same community are more likely to share common links, we explore community structure heuristically by giving priority to vertices which have a high link similarity with the community. A three-phase process is also used for the sake of improving quality of community structure. Experimental results prove that our method performs effectively not only in computer-generated graphs but also in real-world graphs.
文摘The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals been readily available for analysis. Two popular topics in the study of social networks are community detection and sentiment analysis. Community detection seeks to find groups of associated individuals within networks, and sentiment analysis attempts to determine how individuals are feeling. While these are generally treated as separate issues, this study takes an integrative approach and uses community detection output to enable community-level sentiment analysis. Community detection is performed using the Walktrap algorithm on a network of Twitter users associated with Microsoft Corporation’s @technet account. This Twitter account is one of several used by Microsoft Corporation primarily for communicating with information technology professionals. Once community detection is finished, sentiment in the tweets produced by each of the communities detected in this network is analyzed based on word sentiment scores from the well-known SentiWordNet lexicon. The combination of sentiment analysis with community detection permits multilevel exploration of sentiment information within the @technet network, and demonstrates the power of combining these two techniques.
基金This research was supported by the Ministry of Trade,Industry&Energy(MOTIE,Korea)under the Industrial Technology Innovation Program,No.10063130by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2019R1A2C1006159)by the Ministry of Science and ICT(MSIT),Korea,under the Information Technology Research Center(ITRC)support program(IITP-2019-2016-0-00313)supervised by the Institute for Information&communications Technology Promotion(IITP).
文摘Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very important.In addition,the similarity among nodes inside the same cluster is greater than among nodes from other clusters.Lately,the global and local methods of community detection have been getting more attention.Therefore,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information.Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation.This process is conducted with the same color till nodes reach maximum similarity.Finally,the communities are formed,and a clear community graph is displayed to the user.Our proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,etc.We perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art models.The results prove that our model is efficient in all aspects,because it quickly identifies communities in the network.Moreover,it can easily be used for friendship recommendations or in business recommendation systems.
文摘Community based churn prediction,or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries.While churn prediction until recent times have focused only on transactional dataset(targeted approach),the untargeted approach through product advisement,digital marketing and expressions in customer’s opinion on the social media like Twitter,have not been fully harnessed.Although this data source has become an important influencing factor with lasting impact on churn management.Since Social Network Analysis(SNA)has become a blended approach for churn prediction and management in modern era,customers residing online predominantly and collectively decide and determines the momentum of churn prediction,retention and decision support.In existing SNA approaches,customers are classified as churner or non-churner(1 or 0).Oftentimes,the customer’s opinion is also neglected and the network structure of community members are not exploited.Consequently,the pattern and influential abilities of customers’opinion on relative members of the community are not analysed.Thus,the research developed a Churn Service Information Graph(CSIG)to define a quadruple churn category(churner,potential churner,inertia customer,premium customer)for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA.The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group.Consequently,every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition,retention and/or decision supports in churn management.
基金supported by the National Natural Science Foundation of China(Grant No.:70973093)the Fundamental Research Funds for the Central Universities(Grant No.:201110401020006)
文摘Sciencenet.cn is the leading online portal serving the Chinese scientific community. This paper intends to analyze the interdisciplinary and intradisciplinary knowledge communication patterns based on friends-list links in the blog community at Sciencenet.cn by using hyperlink analysis and social network analysis. The major findings are: 1) More bloggers have an academic background in management science and life science; 2) there are some core actors in co-inlink network and co-outlink network, who take the lead in engaging with knowledge exchange activities and produce a great influence on interdisciplinary communication; 3) interactive relationships commonly exist between a blogger and those on his/her friends list, and the most linked-to blogs usually play a key role in generating interactive communication; 4) management science has the highest co-inlink count with life science or information science and it has the highest co-outlink count with life science or mathematical and physical science; 5) management science and life science have the greatest impact on information science and the interdisciplinary knowledge communication will also produce relatively significant influence on the development of information science discipline. It is our hope that this research can serve as a reference source for the future studies of academic virtual communities, and the development of mechanisms for facilitating increased engagement in knowledge exchange activities in academic virtual communities.
基金the National Natural Science Foundation of China under Grant No.60402011the National Science and Technology Support Program of China under Grant No.2006BAII03B05.
文摘With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in physics sociology, computer society, etc. Although this investigation of community structures has motivated many diverse algorithms, most of them are unsuitable when dealing with large networks due to their computational cost. In this paper, we present a faster algorithm ComTector which is more efficient for the community detection in large complex networks based on the nature of overlapping cliques. This algorithm does not require any priori knowledge about the number or the original division of the communities. With respect to practical applications, ComTector is challenging with five different types of networks including the classic Zachary Karate Club, Scientific Collaboration Network South Florida Free Word Association Network, Urban Traffic Network North America Power Grid and the Telecommunication Call Network. Experimental results show that our algorithm can discover meaningful communities that meet both the objective basis and our intuitions.
基金This work was partly supported by the National Natural Science Foundation of China(Grant Nos.11671400,61672524)National Science Foundation(1747818).
文摘In the field of social network analysis,Link Predic-tion is one of the hottest topics which has been attracted attentions in academia and industry.So far,literatures for solving link prediction can be roughly divided into two categories:similarity-based and learning-based methods.The learning-based methods have higher accuracy,but their time complexities are too high for complex networks.However,the similarity-based methods have the advantage of low time consumption,so improving their accuracy becomes a key issue.In this paper,we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm(SSDBA),In SSDBA,we first detect communities of a social network and identify active nodes based on community average threshold(CAT)and node average threshold(NAT)in each community.Second,we propose the stretch shrink distance(SSD)model to iteratively calculate the changes of distances between active nodes and their local neighbors.Finally,we make predictions when these links'distances tend to converge.Furthermore,extensive parameters learning have been carried out in experiments.We compare our SSDBA with other popular approaches.Experimental results validate the effectiveness and efficiency of proposed algorithm.
文摘Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out usingassociation rule mining or community detection approach. This article usesboth methods to investigate a transaction dataset collected from a brick-andmortargrocery store. The findings reveal interesting purchasing patterns oflocal residents and prompt us to consider dynamic modeling of the productnetwork in the future.