Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining最新文献
J. Brea, Javier Burroni, Martin Minnoni, Carlos Sarraute
We study the structure of the social graph of mobile phone users in the country of Mexico, with a focus on demographic attributes of the users (more specifically the users' age). We examine assortativity patterns in the graph, and observe a strong age homophily in the communications preferences. We propose a graph based algorithm for the prediction of the age of mobile phone users. The algorithm exploits the topology of the mobile phone network, together with a subset of known users ages (seeds), to infer the age of remaining users. We provide the details of the methodology, and show experimental results on a network GT with more than 70 million users. By carefully examining the topological relations of the seeds to the rest of the nodes in GT, we find topological metrics which have a direct influence on the performance of the algorithm. In particular we characterize subsets of users for which the accuracy of the algorithm is 62% when predicting between 4 age categories (whereas a pure random guess would yield an accuracy of 25%). We also show that we can use the probabilistic information computed by the algorithm to further increase its inference power to 72% on a significant subset of users.
{"title":"Harnessing Mobile Phone Social Network Topology to Infer Users Demographic Attributes","authors":"J. Brea, Javier Burroni, Martin Minnoni, Carlos Sarraute","doi":"10.1145/2659480.2659492","DOIUrl":"https://doi.org/10.1145/2659480.2659492","url":null,"abstract":"We study the structure of the social graph of mobile phone users in the country of Mexico, with a focus on demographic attributes of the users (more specifically the users' age). We examine assortativity patterns in the graph, and observe a strong age homophily in the communications preferences. We propose a graph based algorithm for the prediction of the age of mobile phone users. The algorithm exploits the topology of the mobile phone network, together with a subset of known users ages (seeds), to infer the age of remaining users. We provide the details of the methodology, and show experimental results on a network GT with more than 70 million users. By carefully examining the topological relations of the seeds to the rest of the nodes in GT, we find topological metrics which have a direct influence on the performance of the algorithm. In particular we characterize subsets of users for which the accuracy of the algorithm is 62% when predicting between 4 age categories (whereas a pure random guess would yield an accuracy of 25%). We also show that we can use the probabilistic information computed by the algorithm to further increase its inference power to 72% on a significant subset of users.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"7 1","pages":"1:1-1:9"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84302420","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}
Financial trading is a social activity that involves every participant's decision making. Meanwhile, people's online behavior collectively creates the public emotion which affects investors' reactions and hence market movements. This process can be modeled by connecting online social behavior and future trading behavior to better understand mechanisms of the stock movement so as to assist financial decision making. In this paper, we investigate the query information of financially related Wikipedia pages, and show that early signs of trading volume movements can be detected which expose financial risks. We embed this information into a classic pairs trading strategy acting on a large portfolio of stocks. Over 23% profits are seen when testing on the year of 2013 and 20% comes from the inclusion of online social data.
{"title":"Enhancing Financial Decision-Making Using Social Behavior Modeling","authors":"Ruoqian Liu, Ankit Agrawal, W. Liao, A. Choudhary","doi":"10.1145/2659480.2659505","DOIUrl":"https://doi.org/10.1145/2659480.2659505","url":null,"abstract":"Financial trading is a social activity that involves every participant's decision making. Meanwhile, people's online behavior collectively creates the public emotion which affects investors' reactions and hence market movements. This process can be modeled by connecting online social behavior and future trading behavior to better understand mechanisms of the stock movement so as to assist financial decision making. In this paper, we investigate the query information of financially related Wikipedia pages, and show that early signs of trading volume movements can be detected which expose financial risks. We embed this information into a classic pairs trading strategy acting on a large portfolio of stocks. Over 23% profits are seen when testing on the year of 2013 and 20% comes from the inclusion of online social data.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"65 1","pages":"13:1-13:5"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73942937","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}
Recommendation systems leverage future internet services to predict personalized recommendations for products, services, media entities or other offerings. Based on the research and development of the FIcontent 2 initiative, we introduce an approach to compensate Cold Start and Sparsity Problems by analyzing semantics of external textual data, in terms of comments from social networks as well as item reviews from product and rating services. Thereby sentiment analysis and semantic keyword extraction approaches are explained and evaluated by using preliminary implementations. The mined data is transferred into, so called, preference ontologies describing the users interest in automatic analyzed topics and subsequently mapped to the properties of items in order to calculate the associated recommendation value.
{"title":"Preference Ontologies based on Social Media for compensating the Cold Start Problem","authors":"Christopher Krauss, Sascha Braun, S. Arbanowski","doi":"10.1145/2659480.2659504","DOIUrl":"https://doi.org/10.1145/2659480.2659504","url":null,"abstract":"Recommendation systems leverage future internet services to predict personalized recommendations for products, services, media entities or other offerings. Based on the research and development of the FIcontent 2 initiative, we introduce an approach to compensate Cold Start and Sparsity Problems by analyzing semantics of external textual data, in terms of comments from social networks as well as item reviews from product and rating services. Thereby sentiment analysis and semantic keyword extraction approaches are explained and evaluated by using preliminary implementations. The mined data is transferred into, so called, preference ontologies describing the users interest in automatic analyzed topics and subsequently mapped to the properties of items in order to calculate the associated recommendation value.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"73 1","pages":"12:1-12:4"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73799175","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}
Gilbert Badaro, Hazem M. Hajj, A. Haddad, W. El-Hajj, K. Shaban
Recommender systems face performance challenges when dealing with sparse data. This paper addresses these challenges and proposes the use of Harmonic Analysis. The method provides a novel approach to the user-item matrix and extracts the interplay between users and items at multiple resolution levels. New affinity matrices are defined to measure similarities among users, among items, and across items and users. Furthermore, the similarities are assessed at multiple levels of granularity allowing individual and group level similarities. These affinity matrices thus produce multiresolution groupings of items and users, and in turn lead to higher accuracy in matching similar context for ratings, and more accurate prediction of new ratings. Evaluation results show superiority of the approach compared to state of the art solutions.
{"title":"A Multiresolution Approach to Recommender Systems","authors":"Gilbert Badaro, Hazem M. Hajj, A. Haddad, W. El-Hajj, K. Shaban","doi":"10.1145/2659480.2659501","DOIUrl":"https://doi.org/10.1145/2659480.2659501","url":null,"abstract":"Recommender systems face performance challenges when dealing with sparse data. This paper addresses these challenges and proposes the use of Harmonic Analysis. The method provides a novel approach to the user-item matrix and extracts the interplay between users and items at multiple resolution levels. New affinity matrices are defined to measure similarities among users, among items, and across items and users. Furthermore, the similarities are assessed at multiple levels of granularity allowing individual and group level similarities. These affinity matrices thus produce multiresolution groupings of items and users, and in turn lead to higher accuracy in matching similar context for ratings, and more accurate prediction of new ratings. Evaluation results show superiority of the approach compared to state of the art solutions.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"85 1","pages":"9:1-9:5"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78019701","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. P. Appel, Victor F. Cavalcante, Marcos R. Vieira, Vagner Figuerêdo de Santana, R. Paula, Steven K. Tsukamoto
Solving today's problems demands more than the effort of an individual, however, brilliant mind. Collaboration and team work are fundamental skills for tackling such problems. The ability of team members to work together and communicate with one another thus becomes an uppermost concern. In this context, to assemble an effective team requires an approach that goes beyond the analysis of individual skills. This paper proposes and examines the problem that takes into account different skill attributes and social ties to build an interconnected team. Our proposed solution is evaluated by means of building one team to defeat an opposite team defined in the same social network. Our experimental results show that our algorithms produces meaningful socially collaborative skilled teams.
{"title":"Building Socially Connected Skilled Teams to Accomplish Complex Tasks","authors":"A. P. Appel, Victor F. Cavalcante, Marcos R. Vieira, Vagner Figuerêdo de Santana, R. Paula, Steven K. Tsukamoto","doi":"10.1145/2659480.2659500","DOIUrl":"https://doi.org/10.1145/2659480.2659500","url":null,"abstract":"Solving today's problems demands more than the effort of an individual, however, brilliant mind. Collaboration and team work are fundamental skills for tackling such problems. The ability of team members to work together and communicate with one another thus becomes an uppermost concern. In this context, to assemble an effective team requires an approach that goes beyond the analysis of individual skills. This paper proposes and examines the problem that takes into account different skill attributes and social ties to build an interconnected team. Our proposed solution is evaluated by means of building one team to defeat an opposite team defined in the same social network. Our experimental results show that our algorithms produces meaningful socially collaborative skilled teams.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"5 1","pages":"8:1-8:5"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84619116","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}
Manish Gaurav, Amit Srivastava, Anoop Kumar, Scott Miller
In recent years, Twitter has become one of the most important modes for social networking and disseminating content on a variety of topics. It has developed into a popular medium for political discourse and social organization during elections. There has been growing body of literature demonstrating the ability to predict the outcome of elections from Twitter data. This works aims to test the predictive power of Twitter in inferring the winning candidate and vote percentages of the candidates in an election. Our prediction is based on the number of times the name of a candidate is mentioned in tweets prior to elections. We develop new methods to augment the counts by counting not only the presence of candidate's official names but also their aliases and commonly appearing names. In addition, we devised a technique to include relevant and filter irrelevant tweets based on predefined set of keywords. Our approach is successful in predicting the winner of all three presidential elections held in Latin America during the months of February through April, 2013.
{"title":"Leveraging candidate popularity on Twitter to predict election outcome","authors":"Manish Gaurav, Amit Srivastava, Anoop Kumar, Scott Miller","doi":"10.1145/2501025.2501038","DOIUrl":"https://doi.org/10.1145/2501025.2501038","url":null,"abstract":"In recent years, Twitter has become one of the most important modes for social networking and disseminating content on a variety of topics. It has developed into a popular medium for political discourse and social organization during elections. There has been growing body of literature demonstrating the ability to predict the outcome of elections from Twitter data.\u0000 This works aims to test the predictive power of Twitter in inferring the winning candidate and vote percentages of the candidates in an election. Our prediction is based on the number of times the name of a candidate is mentioned in tweets prior to elections. We develop new methods to augment the counts by counting not only the presence of candidate's official names but also their aliases and commonly appearing names. In addition, we devised a technique to include relevant and filter irrelevant tweets based on predefined set of keywords. Our approach is successful in predicting the winner of all three presidential elections held in Latin America during the months of February through April, 2013.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"113 1","pages":"7:1-7:8"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74285126","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 network exhibits a special property: community structure. The community detection on a social network is like clustering on a graph, but the nodes in social network has unique name and the edges has some special properties like friendship, common interest. There have been many clustering methods can be used to detect the community structure on a static network. But in real-world, the social networks are usually dynamic, and the community structures always change over time. We propose Community Update and Tracking algorithm, CUT, to efficiently update and track the community structure algorithm in dynamic social networks. When the social network has some variations in different timestamps, we track the seeds of community and update the community structure instead of recalculating all nodes and edges in the network. The seeds of community is the base of community, we find some nodes which connected together tightly, and these nodes probably become communities. Therefore, our approach can quickly and efficiently update the community structure.
{"title":"CUT: community update and tracking in dynamic social networks","authors":"Hao-Shang Ma, Jen-Wei Huang","doi":"10.1145/2501025.2501026","DOIUrl":"https://doi.org/10.1145/2501025.2501026","url":null,"abstract":"Social network exhibits a special property: community structure. The community detection on a social network is like clustering on a graph, but the nodes in social network has unique name and the edges has some special properties like friendship, common interest. There have been many clustering methods can be used to detect the community structure on a static network. But in real-world, the social networks are usually dynamic, and the community structures always change over time. We propose Community Update and Tracking algorithm, CUT, to efficiently update and track the community structure algorithm in dynamic social networks. When the social network has some variations in different timestamps, we track the seeds of community and update the community structure instead of recalculating all nodes and edges in the network. The seeds of community is the base of community, we find some nodes which connected together tightly, and these nodes probably become communities. Therefore, our approach can quickly and efficiently update the community structure.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"32 5S 1","pages":"6:1-6:8"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80950827","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 recent years due to the rise of social, biological, and other rich content graphs, several new graph clustering methods using structure and node's attributes have been introduced. In this paper, we compare our novel clustering method, termed Selection method, against seven clustering methods: three structure and attribute methods, one structure only method, one attribute only method, and two ensemble methods. The Selection method uses the graph structure ambiguity to switch between structure and attribute clustering methods. We shows that the Selection method out performed the state-of-art structure and attribute methods.
{"title":"Structure and attributes community detection: comparative analysis of composite, ensemble and selection methods","authors":"Haithum Elhadi, G. Agam","doi":"10.1145/2501025.2501034","DOIUrl":"https://doi.org/10.1145/2501025.2501034","url":null,"abstract":"In recent years due to the rise of social, biological, and other rich content graphs, several new graph clustering methods using structure and node's attributes have been introduced. In this paper, we compare our novel clustering method, termed Selection method, against seven clustering methods: three structure and attribute methods, one structure only method, one attribute only method, and two ensemble methods. The Selection method uses the graph structure ambiguity to switch between structure and attribute clustering methods. We shows that the Selection method out performed the state-of-art structure and attribute methods.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"26 1","pages":"10:1-10:7"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88786521","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}
Real-world diffusion phenomena are governed by collective behaviors of individuals, and their underlying connections are not limited to single social networks but are extended to globally interconnected heterogeneous social networks. Different levels of heterogeneity of networks in such global diffusion may also reflect different diffusion processes. In this regard, we focus on uncovering mechanisms of information diffusion across different types of social networks by considering hidden interaction patterns between them. For this study, we propose dual representations of heterogeneous social networks in terms of direct and indirect influence at a macro level. Accordingly, we propose two macro-level diffusion models by extending the Bass model with a probabilistic approach. By conducting experiments on both synthetic and real datasets, we show the feasibility of the proposed models. We find that real-world news diffusion in social media can be better explained by direct than indirect diffusion between different types of social media, such as News, social networking sites (SNS), and Blog media. In addition, we investigate different diffusion patterns across topics. The topics of Politics and Disasters tend to exhibit concurrent and synchronous diffusion by direct influence across social media, leading to high relative entropy of diverse media participation. The Arts and Sports topics show strong interactions within homogeneous networks, while interactions with other social networks are unbalanced and relatively weak, which likely drives lower relative entropy. We expect that the proposed models can provide a way of interpreting strength, directionality, and direct/indirectness of influence between heterogeneous social networks at a macro level.
{"title":"Modeling direct and indirect influence across heterogeneous social networks","authors":"Minkyoung Kim, D. Newth, P. Christen","doi":"10.1145/2501025.2501030","DOIUrl":"https://doi.org/10.1145/2501025.2501030","url":null,"abstract":"Real-world diffusion phenomena are governed by collective behaviors of individuals, and their underlying connections are not limited to single social networks but are extended to globally interconnected heterogeneous social networks. Different levels of heterogeneity of networks in such global diffusion may also reflect different diffusion processes. In this regard, we focus on uncovering mechanisms of information diffusion across different types of social networks by considering hidden interaction patterns between them. For this study, we propose dual representations of heterogeneous social networks in terms of direct and indirect influence at a macro level. Accordingly, we propose two macro-level diffusion models by extending the Bass model with a probabilistic approach. By conducting experiments on both synthetic and real datasets, we show the feasibility of the proposed models. We find that real-world news diffusion in social media can be better explained by direct than indirect diffusion between different types of social media, such as News, social networking sites (SNS), and Blog media. In addition, we investigate different diffusion patterns across topics. The topics of Politics and Disasters tend to exhibit concurrent and synchronous diffusion by direct influence across social media, leading to high relative entropy of diverse media participation. The Arts and Sports topics show strong interactions within homogeneous networks, while interactions with other social networks are unbalanced and relatively weak, which likely drives lower relative entropy. We expect that the proposed models can provide a way of interpreting strength, directionality, and direct/indirectness of influence between heterogeneous social networks at a macro level.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"1 1","pages":"9:1-9:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82326126","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}
Jennifer H. Nguyen, Bo Hu, Stephan Günnemann, M. Ester
The ever rising popularity of online social networks has not only attracted much attention from everyday users but also from academic researchers. In particular, research has been done to investigate the effect of social influence on users' actions on items in the network. However, all social influence research in the data-mining field has been done in a context-independent setting, i.e., irrespective of an item's characteristics. It would be interesting to find the specific contexts in which users influence each other in a similar manner. In this way, applications such as recommendation engines can focus on a specific context for making recommendations. In this paper, we pose the problem of finding contexts of social influence where the social influence is similar across all items in the context. We present a full-space clustering algorithm and a subspace clustering algorithm to find these contexts and test the algorithms on the Digg data set. We demonstrate that our algorithms are capable of finding meaningful contexts of influence in addition to rediscovering the predefined categories specific to the Digg news site.
{"title":"Finding contexts of social influence in online social networks","authors":"Jennifer H. Nguyen, Bo Hu, Stephan Günnemann, M. Ester","doi":"10.1145/2501025.2501028","DOIUrl":"https://doi.org/10.1145/2501025.2501028","url":null,"abstract":"The ever rising popularity of online social networks has not only attracted much attention from everyday users but also from academic researchers. In particular, research has been done to investigate the effect of social influence on users' actions on items in the network. However, all social influence research in the data-mining field has been done in a context-independent setting, i.e., irrespective of an item's characteristics. It would be interesting to find the specific contexts in which users influence each other in a similar manner. In this way, applications such as recommendation engines can focus on a specific context for making recommendations. In this paper, we pose the problem of finding contexts of social influence where the social influence is similar across all items in the context. We present a full-space clustering algorithm and a subspace clustering algorithm to find these contexts and test the algorithms on the Digg data set. We demonstrate that our algorithms are capable of finding meaningful contexts of influence in addition to rediscovering the predefined categories specific to the Digg news site.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"5 1","pages":"1:1-1:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76403242","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}
Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining
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