![](bg9.png)
Table 3: Result summary on benchmark datasets. The hidden dimension was set to 5 for all the methods. The bold faces
indicate the approach with the best score on each dataset. We include the detailed statistics of Drug and Course in the
appendix.
Datasets Metric GCMC TOP GRMF PMF CF-NADE RGCNN
MovieLens-100K
RMSE 0.9641 1.0276 0.9498 0.9907 0.9917 0.9600
NDCG@3 73.52 75.00 74.88 74.73 66.84 74.45
Cora
MAP 18.34 13.89 5.38 8.62 12.85 9.94
NDCG@3 16.80 12.17 4.27 7.67 10.99 9.45
Citeseer
MAP 15.14 11.20 3.53 7.22 8.97 7.00
NDCG@3 13.82 9.84 2.49 6.24 7.19 6.22
Course
MAP 46.02 36.56 34.32 31.23 31.62 31.30
NDCG@3 44.62 34.09 31.00 27.82 26.77 26.67
Drug
MAP 35.02 34.33 31.35 29.81 25.73 24.31
NDCG@3 30.96 30.03 26.77 24.78 20.97 19.27
6 CONCLUSION
In this paper we presented a new approach to the bi-
partite edge prediction problem, which uses a multi-
hop neural network structure to effectively enrich
the model expressiveness, and the first-order Chebys-
hev approximation to substantially reduce the com-
plexity of training time. We also employ a low-
rank prior in the input signals so as to make ro-
bust prediction. Our approach consistently outper-
formed several state-of-the-art methods in our expe-
riments on the benchmark datasets for collaborative
filtering, citation network analysis, course prerequi-
site prediction and drug-target interaction prediction
in most cases.
ACKNOWLEDGEMENTS
This work is supported in part by the National Science
Foundation (NSF) under grant IIS-1546329.
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