Heterogenous Graph Mining for Measuring the Impact of Research Institutions
Zeyu Qiu1,2; Deqiang Kong3; Zhenfeng Zhu3; Hanqing Lu1,2; Jian Cheng1,2
2016-08
会议名称The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD, KDD Cup workshop)
会议日期2016-8
会议地点San Fancisco, California
摘要Mining 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.
关键词Social Network Feature Engineering Model Selection Decision Tree
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14560
专题模式识别国家重点实验室_图像与视频分析
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Beijing Jiaotong University
推荐引用方式
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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