Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this approach's success is capped by the proportion of links that are formed among existing nodes embedded in these properties. Consequentially, sustained ties as well as links that involve one or two new network participants are typically not predicted. The purpose of this study is to highlight formational constraints that need to be considered to increase the practical value of link prediction methods for collaboration networks. In this study, we identify the distribution of basic link formation types based on four large-scale, over-time collaboration networks, showing that current link predictors can maximally anticipate around 25% of links that involve at least one prior network member. This implies that for collaboration networks, increasing the accuracy of computational link prediction solutions may not be a reasonable goal when the ratio of collaboration ties that are eligible to the classic link prediction process is low.
IGA-LWP: An Iterative Gradient-based Adversarial Attack for Link Weight Prediction
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）
A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature
Chengdu Library and Information Center, Chinese Academy of Sciences（中国科学院成都文献情报中心）；University of Michigan（密歇根大学）；Shandong University of Technology（山东理工大学）；Kunming University of Science and Technology（昆明理工大学）；Macau University of Science and Technology（澳门科技大学）；Purdue University（普渡大学）
Research networks generated by organizational structures, co-authorships and citations: A Case Study of German Centre for Integrative Biodiversity Research (iDiv)
Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More
The Chinese University of Hong Kong（香港中文大学）；University of Hong Kong（香港大学）；Sun Yat-sen University（中山大学）；Chinese University of Hong Kong(Shenzhen)（香港中文大学（深圳））；Hong Kong University of Science and Technology（香港科技大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
