Information Technology Reference
In-Depth Information
for individuals (previously for link prediction in [5]) and address various structural
features from multiple networks systematically for learning real world rankings (i.e.
target rankings).
7Conluion
This paper described methods of learning the ranking of entities from social net-
works mined from the web. We first extracted social networks of different kinds
from the web. Subsequently, we used these networks and a given target ranking to
learn a ranking model. We proposed an algorithm to learn the model by integrating
network-based features from a given social network that was mined from the web.
We proposed three approaches used to obtain the ranking model. Results of exper-
iments on a researcher field reveal the effectiveness of our models for explaining
a target ranking of researchers' productivity using multiple social networks mined
from the web. The results underscore the usefulness of our approach, with which
we can elucidate important relations as well as important structural embeddedness
to predict the rankings. Our model provides an example of advanced use of a so-
cial network mined from the web. More networks and attributes for various target
rankings in different domains can be designated for improving the usefulness of our
models in the future.
References
1. Agarwal, A., Chakrabarti, S., Aggarwal, S.: Learning to rank networked entities. In:
The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data
mining, Philadelphia, USA (2006)
2. Backstrom, L., Huttenlocher, D., Lan, X., Kleinberg, J.: Group formation in large social
networks: Membership, Growth, and Evolution. In: 12th ACM SIGKDD International
Conference on Knowledge Discovery and Data mining, Philadelphia, USA (2006)
3. Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Mining Hidden Community in Heterogeneous
Social Networks. In: Proceedings of the ACM Workshop on Link Analysis and Group
Detection, Chicago, USA (2005)
4. Jin, Y., Matsuo, Y., Ishizuka, M.: Extracting Social Networks Among Various Entities on
the Web. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp.
251-266. Springer, Heidelberg (2007)
5. Karamon, J., Matsuo, Y., Ishizuka, M.: Generating Useful Network-based Features for
Analyzing Social Network. In: 23rd Conference on Artificial Intelligence, Chicago, USA
(2008)
6. Jon, K.M.: Authoritative Sources in a Hyperlinked Environment. In: Proceedings of
ACM-SIAM Symposium on Discrete Algorithms, pp. 668-677 (1998)
7. Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In:
12th International Conference on Information and Knowledge Management, New Or-
leans, LA, USA (2003)
8. Matsuo, Y., Mori, J., Hamasaki, M., Ishida, K., Nishimura, T., Takeda, H., Hasida, K.,
Ishizuka, M.: POLYPHONET: an advanced social network extraction system. In: 15th
International World Wide Web Conference, Edinburgh, Scotland (2006)
