Online social networks are mainly characterized by large-scale and heterogeneous semantic relationships. Unfortunately, for online social network services such as Facebook or Twitter, it is very difficult to obtain the fully observed network without privilege to access the data internally. To address the above needs, social network sampling is a means that aims at identifying a representative sub graph that preserves certain properties of the network, given the information of any instance in the network is unknown before being sampled. This study tackles heterogeneous network sampling by considering the conditional dependency of node types and link types, where we design a property, Relational Profile, to account such characterization. We further propose a sampling method to preserve this property. Lastly, we propose to evaluate our model from three different angles. First, we show that the proposed sampling method can more faithfully preserve the Relational Profile. Second, we evaluate the usefulness of the Relational Profile showing such information is beneficial for link prediction tasks. Finally, we evaluate whether the networks sampled by our method can be used to train more accurate prediction models comparing to networks produced by other methods.
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Publication status||Published - 2013 Dec 1|
|Event||13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States|
Duration: 2013 Dec 7 → 2013 Dec 10
All Science Journal Classification (ASJC) codes