Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy
Proceedings of the 21st ACM conference on Hypertext and hypermedia, Toronto, Canada, (2010)Abstract
The ongoing spread of online social networking and sharing
sites has reshaped the way how people interact with each
other. Analyzing the relatedness of di erent users within the
resulting large populations of these systems plays an impor-
tant role for tasks like user recommendation or community
detection. Algorithms in these elds typically face the pro-
blem that explicit user relationships (like friend lists) are
often very sparse. Surprisingly, implicit evidences (like click
logs) of user relations have hardly been considered.
Based on our long-time experience with running the social
bookmark and publication sharing platform BibSonomy 4,
we identify in this paper di erent evidence networks of user
relationships in our system. We broadly classify each net-
work based on whether the links are explicitly established
by the users (e. g., friendship or group membership) or ac-
crue implicitly in the running system (e. g., when user u
copies an entry of user v). We systematically analyze struc-
tural 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 di erent characteristics and provide
preparatory work for the inclusion of new (and less spar-
se) information into the process of optimizing community
detection or user recommendation algorithms
