Heterogenous Graph Mining for Measuring the Impact of Research Institutions

	Heterogenous Graph Mining for Measuring the Impact of Research Institutions
	Zeyu Qiu1,2 ; Deqiang Kong 3; Zhenfeng Zhu 3; 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	紫东太初大模型研究中心_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
第一作者单位	中国科学院自动化研究所
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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文件名称/大小	文献类型	版本类型	开放类型	使用许可
qzy_KDDCup_2016_pape（906KB）	会议论文		开放获取	CC BY-NC-SA	浏览