5 research outputs found
Detecting Friendship Within Dynamic Online Interaction Networks
In many complex social systems, the timing and frequency of interactions
between individuals are observable but friendship ties are hidden. Recovering
these hidden ties, particularly for casual users who are relatively less
active, would enable a wide variety of friendship-aware applications in domains
where labeled data are often unavailable, including online advertising and
national security. Here, we investigate the accuracy of multiple statistical
features, based either purely on temporal interaction patterns or on the
cooperative nature of the interactions, for automatically extracting latent
social ties. Using self-reported friendship and non-friendship labels derived
from an anonymous online survey, we learn highly accurate predictors for
recovering hidden friendships within a massive online data set encompassing 18
billion interactions among 17 million individuals of the popular online game
Halo: Reach. We find that the accuracy of many features improves as more data
accumulates, and cooperative features are generally reliable. However,
periodicities in interaction time series are sufficient to correctly classify
95% of ties, even for casual users. These results clarify the nature of
friendship in online social environments and suggest new opportunities and new
privacy concerns for friendship-aware applications that do not require the
disclosure of private friendship information.Comment: To Appear at the 7th International AAAI Conference on Weblogs and
Social Media (ICWSM '13), 11 pages, 1 table, 6 figure
Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks
On-line social networks (OSNs) often contain many different types of
relationships between users. When studying the structure of OSNs such as
Facebook, two of the most commonly studied networks are friendship and
interaction networks. The link prediction problem in friendship networks has
been heavily studied. There has also been prior work on link prediction in
interaction networks, independent of friendship networks. In this paper, we
study the predictive power of combining friendship and interaction networks. We
hypothesize that, by leveraging friendship networks, we can improve the
accuracy of link prediction in interaction networks. We augment several
interaction link prediction algorithms to incorporate friendships and predicted
friendships. From experiments on Facebook data, we find that incorporating
friendships into interaction link prediction algorithms results in higher
accuracy, but incorporating predicted friendships does not when compared to
incorporating current friendships.Comment: To appear in ICWSM 2018. This version corrects some minor errors in
Table 1. MATLAB code available at
https://github.com/IdeasLabUT/Friendship-Interaction-Predictio
Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network
Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynamic networks is that choosing a short temporal window may lead to chaotic changes in network topology and measures (for example, the actors’ centrality measures and the average path length); however, choosing a long window may compromise the study and the investigation of network dynamics. Therefore, to facilitate the analysis and understand different patterns of actor-oriented evolutionary aspects, it is necessary to define an optimal window length (temporal duration) with which to sample a dynamic network. In addition to determining the optical temporal duration, another key task for understanding the dynamics of evolving networks is being able to predict the likelihood of future links among pairs of actors given the existing states of link structure at present time. This phenomenon is known as the link prediction problem in network science. Instead of considering a static state of a network where the associated topology does not change, dynamic link prediction attempts to predict emerging links by considering different types of historical/temporal information, for example the different types of temporal evolutions experienced by the actors in a dynamic network due to the topological evolution over time, known as actor dynamicities. Although there has been some success in developing various methodologies and metrics for the purpose of dynamic link prediction, mining actor-oriented evolutions to address this problem has received little attention from the research community. In addition to this, the existing methodologies were developed without considering the sampling window size of the dynamic network, even though the sampling duration has a large impact on mining the network dynamics of an evolutionary network. Therefore, although the principal focus of this thesis is link prediction in dynamic networks, the optimal sampling window determination was also considered