Name
Abstract | ||
|---|---|---|
Who are the best targets to receive a call-for-paper or call-for-participation? What kind of topics should we propose for a workshop or a special issue of next year? Precisely predicting author's topic following behavior, i.e., publishing papers of a certain research topic in future, is essential to answer these questions. In this paper, we aim to model and predict author's topic following behavior in a heterogeneous information network. The heart of our methodology is to evaluate the author-author similarity through informative meta paths in the network. The models we propose in this paper can predict not only whether a given author will follow a certain topic but also the topic distribution over all publications in the next year. Extensive experimental evaluations justify that the prediction performance of our approach outperforms the existing approaches across various topics. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2808797.2809417 | ASONAM |
Keywords | Field | DocType |
topic following, heterogenous information networks, meta path | Data science,Data mining,Information networks,Computer science,Feature extraction,Artificial intelligence,Heterogeneous network,Publishing,Machine learning,Semantics | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deqing Yang | 1 | 29 | 9.69 |
| Yanghua Xiao | 2 | 482 | 54.90 |
| Hanghang Tong | 3 | 3560 | 202.37 |
| Wanyun Cui | 4 | 127 | 7.47 |
| Wei Wang | 5 | 382 | 21.84 |