Popular social networking sites have revolutionized the way people interact on the Web, enabling rapid information dissemination and search. In an enterprise, understanding how information flows within and between organizational levels and business units is of great importance. Despite numerous studies in information diffusion in online social networks, little is known about factors that affect the dynamics of technological adoption at the workplace. Here, we address this problem, by examining the impact of organizational hierarchy in adopting new technologies in the enterprise. Our study suggests that middle-level managers are more successful in influencing employees into adopting a new microblogging service. Further, we reveal two distinct patterns of peer pressure, based on which employees are not only more likely to adopt the service, but the rate at which they do so quickens as the popularity of the new technology increases. We integrate our findings into two intuitive, realistic agent-based computational models that capture the dynamics of adoption at both microscopic and macroscopic levels. We evaluate our models in a real-world dataset we collected from a multinational Fortune 500 company. Prediction results show that our models provide great improvements over commonly used diffusion models. Our findings provide significant insights to managers seeking to realize the dynamics of adoption of new technologies in their company, and could assist in designing better strategies for rapid and efficient technology adoption and information dissemination at the workplace.
{"title":"The role of organization hierarchy in technology adoption at the workplace","authors":"C. Chelmis, V. Prasanna","doi":"10.1145/2492517.2492566","DOIUrl":"https://doi.org/10.1145/2492517.2492566","url":null,"abstract":"Popular social networking sites have revolutionized the way people interact on the Web, enabling rapid information dissemination and search. In an enterprise, understanding how information flows within and between organizational levels and business units is of great importance. Despite numerous studies in information diffusion in online social networks, little is known about factors that affect the dynamics of technological adoption at the workplace. Here, we address this problem, by examining the impact of organizational hierarchy in adopting new technologies in the enterprise. Our study suggests that middle-level managers are more successful in influencing employees into adopting a new microblogging service. Further, we reveal two distinct patterns of peer pressure, based on which employees are not only more likely to adopt the service, but the rate at which they do so quickens as the popularity of the new technology increases. We integrate our findings into two intuitive, realistic agent-based computational models that capture the dynamics of adoption at both microscopic and macroscopic levels. We evaluate our models in a real-world dataset we collected from a multinational Fortune 500 company. Prediction results show that our models provide great improvements over commonly used diffusion models. Our findings provide significant insights to managers seeking to realize the dynamics of adoption of new technologies in their company, and could assist in designing better strategies for rapid and efficient technology adoption and information dissemination at the workplace.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"38 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":"129683819","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}
Anika Gupta, K. Sycara, Geoffrey J. Gordon, Ahmed S. Hefny
What does a user do when he logs in to the Twitter website? Does he merely browse through the tweets of all his friends as a source of information for his own tweets, or does he simply tweet a message of his own personal interest? Does he skim through the tweets of all his friends or only of a selected few? A number of factors might influence a user in these decisions. Does this social influence vary across cultures? In our work, we propose a simple yet effective model to predict the behavior of a user - in terms of which hashtag or named entity he might include in his future tweets. We have approached the problem as a classification task with the various influences contributing as features. Further, we analyze the contribution of the weights of the different features. Using our model we analyze data from different cultures and discover interesting differences in social influence.
{"title":"Exploring friend's influence in cultures in Twitter","authors":"Anika Gupta, K. Sycara, Geoffrey J. Gordon, Ahmed S. Hefny","doi":"10.1145/2492517.2492549","DOIUrl":"https://doi.org/10.1145/2492517.2492549","url":null,"abstract":"What does a user do when he logs in to the Twitter website? Does he merely browse through the tweets of all his friends as a source of information for his own tweets, or does he simply tweet a message of his own personal interest? Does he skim through the tweets of all his friends or only of a selected few? A number of factors might influence a user in these decisions. Does this social influence vary across cultures? In our work, we propose a simple yet effective model to predict the behavior of a user - in terms of which hashtag or named entity he might include in his future tweets. We have approached the problem as a classification task with the various influences contributing as features. Further, we analyze the contribution of the weights of the different features. Using our model we analyze data from different cultures and discover interesting differences in social influence.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"15 3-4 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":"127521425","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}
Ever-increasing emergence of location-aware ubiquitous devices has facilitated collection of time-stamped mobility data. This large volume of data not only provides trajectory information but also information about social interaction between individuals. Unlike trajectory representation and discovery, discovery of social ties and interactions hidden in mobility data has not yet been fully explored. To identify such interaction, social network analysis has been recently used. However, compared with data from emails, phone calls, and messages, which are commonly used for social network analysis, mobility data convey less information about interaction between entities. Therefore, identifying the type of tie between two entities using only mobility data is a great challenge. In this paper, we propose a method for measuring the strength and type of social ties between people only based on their spatio-temporal correlations. Using mutual information metric, we propose utilization of two types of measures for identifying the purpose of being in a certain location. Our experimental results using a location-aware sensing device show that our method can identify different social ties between various entities successfully.
{"title":"On the use of mobility data for discovery and description of social ties","authors":"Mitra Baratchi, N. Meratnia, P. Havinga","doi":"10.1145/2492517.2500263","DOIUrl":"https://doi.org/10.1145/2492517.2500263","url":null,"abstract":"Ever-increasing emergence of location-aware ubiquitous devices has facilitated collection of time-stamped mobility data. This large volume of data not only provides trajectory information but also information about social interaction between individuals. Unlike trajectory representation and discovery, discovery of social ties and interactions hidden in mobility data has not yet been fully explored. To identify such interaction, social network analysis has been recently used. However, compared with data from emails, phone calls, and messages, which are commonly used for social network analysis, mobility data convey less information about interaction between entities. Therefore, identifying the type of tie between two entities using only mobility data is a great challenge. In this paper, we propose a method for measuring the strength and type of social ties between people only based on their spatio-temporal correlations. Using mutual information metric, we propose utilization of two types of measures for identifying the purpose of being in a certain location. Our experimental results using a location-aware sensing device show that our method can identify different social ties between various entities successfully.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"138 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":"131942346","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}
Most of the criminals choose each other to commit crime together. They choose each other based on their similarity or need for particular skills and expertise. Research shows that some features of crime and criminals are important for their decisions to commit crime together. Co-offending history of criminals and similarity of hometown and kinship between criminals are important. Choice of crime location, time and similarity of crime committing methods between criminals are other important factors. To see which crime features are important for committing crime together, two data sets which contain thousands of crimes and hundreds of criminals records, are tested for which features are the most important for criminal network members to work together.
{"title":"Which crime features are important for criminal network members?","authors":"Fatih Özgül, Z. Erdem","doi":"10.1145/2492517.2500319","DOIUrl":"https://doi.org/10.1145/2492517.2500319","url":null,"abstract":"Most of the criminals choose each other to commit crime together. They choose each other based on their similarity or need for particular skills and expertise. Research shows that some features of crime and criminals are important for their decisions to commit crime together. Co-offending history of criminals and similarity of hometown and kinship between criminals are important. Choice of crime location, time and similarity of crime committing methods between criminals are other important factors. To see which crime features are important for committing crime together, two data sets which contain thousands of crimes and hundreds of criminals records, are tested for which features are the most important for criminal network members to work together.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"9 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":"127944885","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}
Mohammad Moshirpour, Shimaa M. El-Sherif, R. Alhajj, B. Far
Social networks are ever-growing systems by inheritance. The increase in the number nodes in these systems often brings forth the need to add additional functionalities. However due to the distributed nature of social networks, system growth can be a challenging task. Therefore scalability of the system is of vital importance in the design of social networks. This research attempts to establish a comprehensive framework for analysis and validation of requirements and design documents for software systems. In previous work, we applied this framework to analyze the requirements of a social network of agents; expressed using scenario-based specifications. Scenarios are appealing because of their expressive power and simplicity. Moreover due to the clear and concise notation of scenarios, they can be used to analyze the system requirements for general validity, lack of deadlock, and existence of emergent behavior. In this paper a methodology to analyze the scalability of social networks is presented. This methodology is devised to indicate whether or not the new requirements of the system are consistent with the current requirements in place. A larger prototype of a social network of MSA for semantic search is utilized to illustrate the developed methodology.
{"title":"Analyzing the scalability of a social network of agents","authors":"Mohammad Moshirpour, Shimaa M. El-Sherif, R. Alhajj, B. Far","doi":"10.1145/2492517.2492571","DOIUrl":"https://doi.org/10.1145/2492517.2492571","url":null,"abstract":"Social networks are ever-growing systems by inheritance. The increase in the number nodes in these systems often brings forth the need to add additional functionalities. However due to the distributed nature of social networks, system growth can be a challenging task. Therefore scalability of the system is of vital importance in the design of social networks. This research attempts to establish a comprehensive framework for analysis and validation of requirements and design documents for software systems. In previous work, we applied this framework to analyze the requirements of a social network of agents; expressed using scenario-based specifications. Scenarios are appealing because of their expressive power and simplicity. Moreover due to the clear and concise notation of scenarios, they can be used to analyze the system requirements for general validity, lack of deadlock, and existence of emergent behavior. In this paper a methodology to analyze the scalability of social networks is presented. This methodology is devised to indicate whether or not the new requirements of the system are consistent with the current requirements in place. A larger prototype of a social network of MSA for semantic search is utilized to illustrate the developed methodology.","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":"131621748","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}
Kathy Macropol, Petko Bogdanov, Ambuj K. Singh, L. Petzold, Xifeng Yan
The study of influence, persuasion, and user sentiment dynamics within online communities has recently emerged as a highly active area of research. In this paper, we focus on analyzing and modeling user sentiment dynamics within a real-world social media such as Twitter. Beyond text and connectivity, we are interested in exploring the level of topical user posting activity and its effect on sentiment change. We perform topic-wise analysis of tweeting behavior that reveals a strong relationship between users' activity acceleration and topic sentiment change. Inspired by this empirical observation, we develop a new generative and predictive model that extends classical neighborhood-based influence propagation with the notion of user activation. We fit the parameters of our model to a large, real-world Twitter dataset and evaluate its utility to predict future sentiment change. Our model outperforms significantly (1 order of magnitude in accuracy) existing alternatives in identifying the individuals who are most likely to change sentiment based on past information. When predicting the next sentiment of users who actually change their opinion (a relatively rare event), our model is twice more accurate than alternatives, while its overall network accuracy is 94% on average. We also study the effect of inactive users on consensus efficiency in the opinion dynamics process both analytically and in simulation within the context of our model.
{"title":"I act, therefore I judge: Network sentiment dynamics based on user activity change","authors":"Kathy Macropol, Petko Bogdanov, Ambuj K. Singh, L. Petzold, Xifeng Yan","doi":"10.1145/2492517.2492623","DOIUrl":"https://doi.org/10.1145/2492517.2492623","url":null,"abstract":"The study of influence, persuasion, and user sentiment dynamics within online communities has recently emerged as a highly active area of research. In this paper, we focus on analyzing and modeling user sentiment dynamics within a real-world social media such as Twitter. Beyond text and connectivity, we are interested in exploring the level of topical user posting activity and its effect on sentiment change. We perform topic-wise analysis of tweeting behavior that reveals a strong relationship between users' activity acceleration and topic sentiment change. Inspired by this empirical observation, we develop a new generative and predictive model that extends classical neighborhood-based influence propagation with the notion of user activation. We fit the parameters of our model to a large, real-world Twitter dataset and evaluate its utility to predict future sentiment change. Our model outperforms significantly (1 order of magnitude in accuracy) existing alternatives in identifying the individuals who are most likely to change sentiment based on past information. When predicting the next sentiment of users who actually change their opinion (a relatively rare event), our model is twice more accurate than alternatives, while its overall network accuracy is 94% on average. We also study the effect of inactive users on consensus efficiency in the opinion dynamics process both analytically and in simulation within the context of our model.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"134 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":"129297277","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}
Mengjuan Liu, Zhuo Zhang, P. Hui, Yujie Qin, S. Kulkarni
Recently, Cyberlocker services have gained great popularity in the file-sharing market. Driven by tremendous benefits a large number of files such as popular movies are uploaded to Cyberlockers. We explore the profit chain of file-sharing networks based on Cyberlockers and find that an important issue is how to collect the download URLs of popular files stored at different Cyberlockers and share them with public users. In this paper, we focus on these sites collecting and sharing the Cyberlocker URLs of movies, called Cyberlocker URL-sharing sites. First, we extract 1,587 URL-sharing sites based on 31,525 valid pages returned by Google search and demonstrate that the quality distribution of these sites follows a power-law. Second, we analyze the link citations among URL-sharing sites and build the directed link citation graph. By characterizing basic metrics of the graph, such as cited strength and in/out-degree, we understand the structure of URL-sharing sites in depth. Furthermore, we discover that Cyberlocker URLs can be disseminated dynamically through crawler mechanisms among different sites, and highlight the implications of such metrics in this context. Additionally, we study the security risks of 1,587 URL-sharing sites. The results show that security risks do exist when surfing 155 suspicious URL-sharing sites such as myrls.me and rapid4me.com although the majority sites (90.23%) are safe. Finally, some preliminary suggestions are discussed from the industry point of view for how to improve the effectiveness of searching, collecting and disseminating Cyberlocker URLs. To the best of our knowledge, this is the first work on the measurement and understanding of Cyberlocker URL-sharing sites.
{"title":"Measurement and understanding of Cyberlocker URL-sharing sites: Focus on movie files","authors":"Mengjuan Liu, Zhuo Zhang, P. Hui, Yujie Qin, S. Kulkarni","doi":"10.1145/2492517.2500303","DOIUrl":"https://doi.org/10.1145/2492517.2500303","url":null,"abstract":"Recently, Cyberlocker services have gained great popularity in the file-sharing market. Driven by tremendous benefits a large number of files such as popular movies are uploaded to Cyberlockers. We explore the profit chain of file-sharing networks based on Cyberlockers and find that an important issue is how to collect the download URLs of popular files stored at different Cyberlockers and share them with public users. In this paper, we focus on these sites collecting and sharing the Cyberlocker URLs of movies, called Cyberlocker URL-sharing sites. First, we extract 1,587 URL-sharing sites based on 31,525 valid pages returned by Google search and demonstrate that the quality distribution of these sites follows a power-law. Second, we analyze the link citations among URL-sharing sites and build the directed link citation graph. By characterizing basic metrics of the graph, such as cited strength and in/out-degree, we understand the structure of URL-sharing sites in depth. Furthermore, we discover that Cyberlocker URLs can be disseminated dynamically through crawler mechanisms among different sites, and highlight the implications of such metrics in this context. Additionally, we study the security risks of 1,587 URL-sharing sites. The results show that security risks do exist when surfing 155 suspicious URL-sharing sites such as myrls.me and rapid4me.com although the majority sites (90.23%) are safe. Finally, some preliminary suggestions are discussed from the industry point of view for how to improve the effectiveness of searching, collecting and disseminating Cyberlocker URLs. To the best of our knowledge, this is the first work on the measurement and understanding of Cyberlocker URL-sharing sites.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"95 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":"126613377","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 this paper, we investigate the recognition of threats in Dutch tweets. As tweets often display irregular grammatical form and deviant orthography, analysis by standard means is problematic. Therefore, we have implemented a new shallow parsing mechanism which is driven by handcrafted rules. Experimental results are encouraging, with an F-measure of about 40% on a random sample of Dutch tweets. Moreover, the error analysis shows some clear avenues for further improvement.
{"title":"Shallow parsing for recognizing threats in Dutch tweets","authors":"Nelleke Oostdijk, H. V. Halteren","doi":"10.1145/2492517.2500271","DOIUrl":"https://doi.org/10.1145/2492517.2500271","url":null,"abstract":"In this paper, we investigate the recognition of threats in Dutch tweets. As tweets often display irregular grammatical form and deviant orthography, analysis by standard means is problematic. Therefore, we have implemented a new shallow parsing mechanism which is driven by handcrafted rules. Experimental results are encouraging, with an F-measure of about 40% on a random sample of Dutch tweets. Moreover, the error analysis shows some clear avenues for further improvement.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"34 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":"126729961","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 address a problem of detecting changes in information posted to social media taking both content and posting time distributions into account. To this end, we introduce a generative model consisting of two components, one for a content distribution and the other for a timing distribution, approximating the shape of the parameter change by a series of step functions. We then propose an efficient algorithm to detect change points by maximizing the likelihood of generating the observed sequence data, which has time complexity almost proportional to the length of observed sequence (possible change points). We experimentally evaluate the method on synthetic data streams and demonstrate the importance of considering both distributions to improve the accuracy. We, further, apply our method to real scoring stream data extracted from a Japanese word-of-mouth communication site for cosmetics and show that it can detect change points and the detected parameter change patterns are interpretable through an in-depth investigation of actual reviews.
{"title":"Detecting changes in content and posting time distributions in social media","authors":"Kazumi Saito, K. Ohara, M. Kimura, H. Motoda","doi":"10.1145/2492517.2492618","DOIUrl":"https://doi.org/10.1145/2492517.2492618","url":null,"abstract":"We address a problem of detecting changes in information posted to social media taking both content and posting time distributions into account. To this end, we introduce a generative model consisting of two components, one for a content distribution and the other for a timing distribution, approximating the shape of the parameter change by a series of step functions. We then propose an efficient algorithm to detect change points by maximizing the likelihood of generating the observed sequence data, which has time complexity almost proportional to the length of observed sequence (possible change points). We experimentally evaluate the method on synthetic data streams and demonstrate the importance of considering both distributions to improve the accuracy. We, further, apply our method to real scoring stream data extracted from a Japanese word-of-mouth communication site for cosmetics and show that it can detect change points and the detected parameter change patterns are interpretable through an in-depth investigation of actual reviews.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"458 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":"120884318","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}
Smart marketing models could utilize communities within the social Web to target advertisements. However, providing accurate community partitions in a reasonable time is challenging for current online large-scale social networks. In this paper, we propose an approach to enhance community detection in online social networks using node similarity techniques. We apply these techniques on unweighted social networks to detect community structure. Our proposed approach creates a virtual network based on the original social network. Virtual edges are added during this pre-processing step based on nodes' similarity in the original social network. Hence, a virtual link is established between any two similar nodes. Then the landmark CNM algorithm is applied on the generated virtual network to detect communities. This approach, labelled Similarity-CNM is expected to further maximize the quality of the inferred communities in terms of modularity and detection speed. Our experimental evaluation study asserts these gains, which accuracy is supported by a study based on Normalized Mutual Information Measure to determine how similar are the actual communities in the original network and the ones found by the proposed approach in this paper.
{"title":"Community detection in social networks through similarity virtual networks","authors":"Kanna AlFalahi, Yacine Atif, S. Harous","doi":"10.1145/2492517.2500299","DOIUrl":"https://doi.org/10.1145/2492517.2500299","url":null,"abstract":"Smart marketing models could utilize communities within the social Web to target advertisements. However, providing accurate community partitions in a reasonable time is challenging for current online large-scale social networks. In this paper, we propose an approach to enhance community detection in online social networks using node similarity techniques. We apply these techniques on unweighted social networks to detect community structure. Our proposed approach creates a virtual network based on the original social network. Virtual edges are added during this pre-processing step based on nodes' similarity in the original social network. Hence, a virtual link is established between any two similar nodes. Then the landmark CNM algorithm is applied on the generated virtual network to detect communities. This approach, labelled Similarity-CNM is expected to further maximize the quality of the inferred communities in terms of modularity and detection speed. Our experimental evaluation study asserts these gains, which accuracy is supported by a study based on Normalized Mutual Information Measure to determine how similar are the actual communities in the original network and the ones found by the proposed approach in this paper.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"8 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":"124926990","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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