KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model | |
Abbas, Khushnood1 ; Shang, Mingsheng2 ; Luo, Xin2; Abbasi, Alireza3
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| 2017-10-15 | |
| 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. |
| 关键词 | Novelty Evolving networks Recommender systems E-commerce Collective behaviour Trend prediction Emerging behaviour |
| DOI | 10.1016/j.physa.2017.04.156 |
| URL | 查看原文 ; 查看原文 |
| 发表期刊 | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
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| ISSN | 0378-4371 |
| 卷号 | 484页码:506-515 |
| 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. |
| 收录类别 | SCI |
| WOS记录号 | WOS:000404823200045 |
| 语种 | 英语 |
