Social Tagging Networks Research Articles

Community Detection Based on Topic Distance in Social Tagging Networks

Research on the community detection in social tagging networks has attracted much attention in the last decade. Extracting the hidden topic information from tags provides a new way of thinking for community detection in social tagging networks. In this paper, a topic tagging network by extracting several topics from the tags through using the Latent Dirichlet Allocation (LDA) model is built firstly. Then a topic distance between users is defined, which depends on the bookmarking relationships between users and tags. Further, a modularity clustering approach based on the topic distance is proposed to detect communities in social tagging networks. Empirical studies on real-world networks demonstrate that the proposed method can effectively detect communities in tagging networks. DOI ; http://dx.doi.org/10.11591/telkomnika.v12i5.4170

HYBRID RECOMMENDATION ALGORITHM BASED ON TWO ROLES OF SOCIAL TAGS

The past few years have witnessed the great success of a new family of paradigms, social tagging networks, which allows users to freely associate social tags to items and efficiently manage them. Thus it provides us a promising way to effectively find useful and interesting information. In this paper, we consider two typical roles of social tags: (i) an accessorial tool helping users organize items; (ii) a bridge that connects users and items. We then propose a hybrid algorithm to integrate the two different roles to obtain better recommendation performance. Experimental results on a real-world data set, Del.icio.us, shows that it can significantly enhance both the algorithmic accuracy and diversity.

Exploiting the Social Tagging Network for Web Clustering

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.

Comparison and Analysis for Social Web Clustering Algorithm

K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This paper compares and analyzes the word vector space model of K-means, and expands it using links information. Then it mines several links involved in social tagging network. Experimental result shows that the proposed social web clustering algorithm is effective.

Improved K-means Clustering Algorithm Based on User Tag

This paper proposed improved K-means clustering algorithm based on user tag. It first used social annotation data to expand the vector space model of K-means. Then, it applied the links involved in social tagging network to enhance the clustering performance. Experimental result shows that the proposed improved K-means clustering algorithm based on user tag is effective.

A hypergraph model of social tagging networks

The past few years have witnessed the great success of a new family of paradigms, so-calledfolksonomy, which allows users to freely associate tags with resources and efficientlymanage them. In order to uncover the underlying structures and user behaviors infolksonomy, in this paper, we propose an evolutionary hypergraph model for explaining theemerging statistical properties. The present model introduces a novel mechanism that cannot only assign tags to resources, but also retrieve resources via collaborativetags. We then compare the model with a real-world data set: Del.icio.us. Indeed,the present model shows considerable agreement with the empirical data in thefollowing aspects: power-law hyperdegree distributions, negative correlation betweenclustering coefficients and hyperdegrees, and small average distances. Furthermore, themodel indicates that most tagging behaviors are motivated by labeling tags onresources, and the tag plays a significant role in effectively retrieving interestingresources and making acquaintances with congenial friends. The proposed model mayshed some light on the in-depth understanding of the structure and function offolksonomy.

Enhancing personalized recommendations on weighted social tagging networks

Recently, social tagging systems have been widely applied in web systems and some physical properties have been found applications in efficiently and effectively personalized recommendation. Social tags can provide highly abstract information about not only item contents but also personalized preferences, hence they might help generate better personalized recommendations. However, how to find out the relevant yet diverse items that are not associated with any tag remains an open question for us. In this paper, we assume a basic attraction may exist for each item. Moreover, considering both personal and global vocabulary, as well as such attractor, we apply diffusion-based recommendation algorithm in weighted social tagging networks. We then evaluate it in a real-world data set Del.icio.us. Experimental results demonstrate that the usage of both tag information and attractor can significantly improve diversity of personalized recommendations, and thus it can be regarded as an alternative recommendation method.