Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Network properties of folksonomies
AI Communications - Network Analysis in Natural Sciences and Engineering
Logsonomy - social information retrieval with logdata
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Social recommender systems for web 2.0 folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
An evaluation study of clustering algorithms in the scope of user communities assessment
Computers & Mathematics with Applications
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Community assessment using evidence networks
MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
Understanding and leveraging tag-based relations in on-line social networks
Proceedings of the 23rd ACM conference on Hypertext and social media
Face-to-face contacts at a conference: dynamics of communities and roles
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
| Hi-index | 0.00 |
The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end. Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users. Our results exhibit different characteristics and. provide preparatory work for the inclusion of new (and less sparse) information into the process of optimizing community detection or user recommendation algorithms.