Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model

doi:10.1016/j.physa.2017.04.156

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model
	Abbas, Khushnood 1; Shang, Mingsheng2 ; Luo, Xin2 ; Abbasi, Alireza 3
刊名	PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
	2017-10-15
卷号	484 页码:506-515
关键词	Novelty Evolving networks Recommender systems E-commerce Collective behaviour Trend prediction Emerging behaviour
ISSN号	0378-4371
DOI	10.1016/j.physa.2017.04.156
通讯作者	Abbas, K (reprint author), Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu 611731, Peoples R China. ; Shang, MS (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 401120, Peoples R China.
英文摘要	Novel phenomenon receives similar attention as popular one. Therefore predicting novelty is as important as popularity. Emergence is the side effect of competition and ageing in evolving systems. Recent behaviour or recent link gain in networks plays an important role in emergence. We exploited this wisdom and came up with two models considering different scenarios and systems. Where recent behaviour dominates over total behaviour (total link gain) in the first one, and recent behaviour is as important as total behaviour for future link gain in the second one. It supposes that random walker walks on a network and can jump to any node, the probability of jumping or making a connection to other node is based on which node is recently more active or receiving more links. In our assumption, the random walker can also jump to the node which is already popular but recently not popular. We are able to predict emerging nodes which are generally suppressed under preferential attachment effect. To show the performance of our model we have conducted experiments on four real data sets namely, MovieLens, Netflix, Facebook and Arxiv High Energy Physics paper citation. For testing our model we used four information retrieval indices namely Precision, Novelty, Area Under Receiving Operating Characteristic (AUC) and Kendal's rank correlation coefficient. We have used four benchmark models for validating our proposed models. Although our model does not perform better in all the cases but, it has theoretical significance in working better for recent behaviour dominated systems. (C) 2017 Elsevier B.V. All rights reserved.
URL标识	查看原文 ; 查看原文
资助项目	National Natural Science Foundation of China[61370150] ; National Natural Science Foundation of China[61433014] ; National Natural Science Foundation of China[91646114]
WOS研究方向	Physics
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000404823200045
内容类型	期刊论文
源URL	[http://172.16.51.4:88/handle/2HOD01W0/139]
专题	大数据挖掘及应用中心
通讯作者	Abbas, Khushnood; Shang, Mingsheng
作者单位	1.Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu 611731, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 401120, Peoples R China 3.Univ New South Wales, UNSW Australia, Sch Engn & IT, Canberra, ACT, Australia
推荐引用方式 GB/T 7714	Abbas, Khushnood,Shang, Mingsheng,Luo, Xin,et al. Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,2017,484:506-515.
APA	Abbas, Khushnood,Shang, Mingsheng,Luo, Xin,&Abbasi, Alireza.(2017).Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model.PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,484,506-515.
MLA	Abbas, Khushnood,et al."Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model".PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 484(2017):506-515.