The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Tianxi Dong - One of the best experts on this subject based on the ideXlab platform.
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modeling topic and community structure in Social Tagging the ttr lda community model
Journal of the Association for Information Science and Technology, 2011Co-Authors: Ying Ding, Zheng QIN, Erjia Yan, Jie Tang, Cassidy R Sugimoto, Nan Lin, Tianxi DongAbstract:The presence of Social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using Social Tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems. © 2011 Wiley Periodicals, Inc.
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Community-based Topic Modeling for Social Tagging
Evaluation, 2010Co-Authors: Daifeng Li, Zheng QIN, Cassidy Sugimoto, Erjia Yan, Juanzi Li, Bing He, Ying Ding, Jie Tang, Tianxi DongAbstract:Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular Social Tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA- Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.
Ying Ding - One of the best experts on this subject based on the ideXlab platform.
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modeling topic and community structure in Social Tagging the ttr lda community model
Journal of the Association for Information Science and Technology, 2011Co-Authors: Ying Ding, Zheng QIN, Erjia Yan, Jie Tang, Cassidy R Sugimoto, Nan Lin, Tianxi DongAbstract:The presence of Social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using Social Tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems. © 2011 Wiley Periodicals, Inc.
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Community-based Topic Modeling for Social Tagging
Evaluation, 2010Co-Authors: Daifeng Li, Zheng QIN, Cassidy Sugimoto, Erjia Yan, Juanzi Li, Bing He, Ying Ding, Jie Tang, Tianxi DongAbstract:Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular Social Tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA- Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.
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perspectives on Social Tagging
Journal of the Association for Information Science and Technology, 2009Co-Authors: Ying Ding, Erjia Yan, Elin K Jacob, Zhixiong Zhang, Schubert Foo, Nicolas L George, Lijiang GuoAbstract:Social Tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of Social Tagging, including different views on the nature of Social Tagging, how to make use of Social tags, and how to bridge Social Tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of Tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze Tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some Tagging patterns and variations are identified and discussed. © 2009 Wiley Periodicals, Inc.
Erjia Yan - One of the best experts on this subject based on the ideXlab platform.
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modeling topic and community structure in Social Tagging the ttr lda community model
Journal of the Association for Information Science and Technology, 2011Co-Authors: Ying Ding, Zheng QIN, Erjia Yan, Jie Tang, Cassidy R Sugimoto, Nan Lin, Tianxi DongAbstract:The presence of Social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using Social Tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems. © 2011 Wiley Periodicals, Inc.
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Community-based Topic Modeling for Social Tagging
Evaluation, 2010Co-Authors: Daifeng Li, Zheng QIN, Cassidy Sugimoto, Erjia Yan, Juanzi Li, Bing He, Ying Ding, Jie Tang, Tianxi DongAbstract:Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular Social Tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA- Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.
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perspectives on Social Tagging
Journal of the Association for Information Science and Technology, 2009Co-Authors: Ying Ding, Erjia Yan, Elin K Jacob, Zhixiong Zhang, Schubert Foo, Nicolas L George, Lijiang GuoAbstract:Social Tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of Social Tagging, including different views on the nature of Social Tagging, how to make use of Social tags, and how to bridge Social Tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of Tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze Tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some Tagging patterns and variations are identified and discussed. © 2009 Wiley Periodicals, Inc.
Jungran Park - One of the best experts on this subject based on the ideXlab platform.
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exploiting the Social Tagging network for web clustering
Systems Man and Cybernetics, 2011Co-Authors: Jungran ParkAbstract:Social Tagging is a major characteristic of Web 2.0. A Social Tagging system can be modeled with a tripartite network of users, resources, and tags. In this paper, we investigate how to enhance Web clustering by leveraging the tripartite network of Social Tagging systems. We propose a clustering method called “Tripartite Clustering” which clusters the three types of nodes (resources, users, and tags) simultaneously by only utilizing the links in the Social Tagging network. We also investigate two other approaches to exploit Social Tagging for clustering with K-means and Link K-means. All the clustering methods are experimented on a real-world Social Tagging data set sampled from del.icio.us. The clustering results are evaluated against a human-maintained Web directory. The experimental results show that the Social Tagging network is a very useful information source for document clustering. All Social-annotation-based clustering methods can significantly improve the performance of content-based clustering. Compared to Social-annotation-based K-means and Link K-means, Tripartite Clustering achieves equivalent or better performance and produces more useful information.
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user tags versus expert assigned subject terms a comparison of librarything tags and library of congress subject headings
Journal of Information Science, 2010Co-Authors: Caimei Lu, Jungran Park, Xiaohua HuAbstract:Social Tagging, as a recent approach for creating metadata, has caught the attention of library and information science researchers. Many researchers recommend incorporating Social Tagging into the library environment and combining folksonomies with formal classification. However, some researchers are concerned with the quality issues of Social annotation because of its uncontrolled nature. In this study, we compare Social tags created by users from the LibraryThing website with the subject terms assigned by experts according to the Library of Congress Subject Headings (LCSH). The purpose of this study is to examine the difference and connections between Social tags and expert-assigned subject terms and further explore the feasibility and obstacles of implementing Social Tagging in library systems. The results of our study show that it is possible to use Social tags to improve the accessibility of library collections. However, the existence of non-subject-related tags may impede the application of Social Tagging in traditional library cataloguing systems.
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the topic perspective model for Social Tagging systems
Knowledge Discovery and Data Mining, 2010Co-Authors: Xin Chen, Jungran ParkAbstract:In this paper, we propose a new probabilistic generative model, called Topic-Perspective Model, for simulating the generation process of Social annotations. Different from other generative models, in our model, the tag generation process is separated from the content term generation process. While content terms are only generated from resource topics, Social tags are generated by resource topics and user perspectives together. The proposed probabilistic model can produce more useful information than any other models proposed before. The parameters learned from this model include: (1) the topical distribution of each document, (2) the perspective distribution of each user, (3) the word distribution of each topic, (4) the tag distribution of each topic, (5) the tag distribution of each user perspective, (6) and the probabilistic of each tag being generated from resource topics or user perspectives. Experimental results show that the proposed model has better generalization performance or tag prediction ability than other two models proposed in previous research.
Bernardo A Huberman - One of the best experts on this subject based on the ideXlab platform.
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semantic stability in Social Tagging streams
The Web Conference, 2014Co-Authors: Claudia Wagner, Philipp Singer, Markus Strohmaier, Bernardo A HubermanAbstract:One potential disadvantage of Social Tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of Social Tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different Social Tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of Social Tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that Tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than Tagging streams which are generated via imitation dynamics or natural language phenomena alone.
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semantic stability in Social Tagging streams
arXiv: Computers and Society, 2013Co-Authors: Claudia Wagner, Philipp Singer, Markus Strohmaier, Bernardo A HubermanAbstract:One potential disadvantage of Social Tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of Social Tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different Social Tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of Social Tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that Tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than Tagging streams which are generated via imitation dynamics or natural language streams alone.
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semantic stability in Social Tagging streams
Social Science Research Network, 2013Co-Authors: Claudia Wagner, Philipp Singer, Markus Strohmaier, Bernardo A HubermanAbstract:One potential disadvantage of Social Tagging systems is that due to the lack of a centralized vocabulary users may never manage to reach a consensus on the description of the entities (e.g., books, user or songs) in the system. Yet, previous research has provided interesting evidence that the tag distributions of entities can become stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the stability of Social Tagging systems in a robust and methodical way? (ii) Does stabilization of tags vary across different Social Tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by making the following contributions: (i) we present a novel and robust method which overcomes a number of limitations in existing methods, (ii) we empirically investigate semantic stabilization processes in a wide range of Social Tagging systems with distinct domains and properties and (iii) we investigate potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language.
