Sampling heterogeneous networks

Cheng Lun Yang, Perng Hwa Kung, Cheng Te Li, Chun An Chen, Shou De Lin

研究成果: Conference article

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號6729629
頁(從 - 到)1247-1252
頁數6
期刊Proceedings - IEEE International Conference on Data Mining, ICDM
DOIs
出版狀態Published - 2013 十二月 1
事件13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
持續時間: 2013 十二月 72013 十二月 10

    指紋

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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