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Heterogenous Graph Mining for Measuring the Impact of Research Institutions
Zeyu Qiu1,2; Deqiang Kong3; Zhenfeng Zhu3; Hanqing Lu1,2; Jian Cheng1,2
Conference NameThe 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD, KDD Cup workshop)
Conference Date2016-8
Conference PlaceSan Fancisco, California
AbstractMining influential nodes in a social network for identifying patterns or maximizing information diffusion has been an active research area with many practical applications. In the research community, influential institutions usually attract denser attention than others. Based on the prediction on how many papers will be accepted by some top conferences held in 2016, the KDD Cup 2016 hosts an international competition for evaluating the importance of academic institutions. This paper describes our solution to the competition. Specifically, the proposed scheme involved in the competition mainly comprises of feature engineering and application of decision tree models. Finally, as claimed by the competition organizer, our approach scored 0.6599, 0.8169, 0.7213 with NDCG@20 in phases 1-3, and resulted in 0.7472 in overall score. With the above scores, our team ranked the first place in phase 2 and fourth place in overall rank.
KeywordSocial Network Feature Engineering Model Selection Decision Tree
Document Type会议论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Beijing Jiaotong University
Recommended Citation
GB/T 7714
Zeyu Qiu,Deqiang Kong,Zhenfeng Zhu,et al. Heterogenous Graph Mining for Measuring the Impact of Research Institutions[C],2016.
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