Investigating the Properties of a Social Bookmarking and Tagging Network

Abstract

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us3 is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make del.icio.us a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly “similar” users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. The authors investigate the question whether friends have common interests, they gain additional insights on the strategies that users use to assign tags to their bookmarks, and they demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

Spatio-temporal small worlds for decentralized information retrieval in social networking

“…Homophily (the tendency of similar people to associate with each other, contributing to triadic closure) [38] and Peer Influence (the influence of persons which are directly linked in the social network) [10] can be prominently attributed for the local homogeneity in terms of information spaces of social groupings. The correlation between social closeness and semantic / topical closeness is also supported by other studies such as [5] and indirectly by [16].…”

Harnessing Folksonomies to Produce a Social Classification of Resources

“…Users tend 4 to annotate the books they own, they have read, or they are planning to read. We believe that users contributing to this kind of sites are more knowledgeable of the resources than those contributing to social bookmarking systems.…”

“…Angelova et al [4] rely on this measure to infer similarity of tags by creating a tag graph, weighting the TF-IDF value of each user to a tag. Shepitsen et al [27] and Liang et al [16] use this measure to represent resources in a recommendation system where resources are recommended to users.…”

“…Systems like 1. http://www.loc.gov/loc/lcib/0302/collections.html Delicious 2 , LibraryThing 3 and GoodReads 4 collect user annotations in the form of tags on their respective collections of resources. These user-generated tags give rise to meaningful data describing the content of the resources [11], [29], [31].…”

Approximate cycles count in undirected graphs

“…Examples of famous social network applications include Paltalk, Hi5, Facebook and mySpace. Such dynamic and complex networks are used to share information and media [1,2] and many applications and systems are developed to mine those…”