School of Computer Science, Zhoukou Normal University（周口师范学院计算机科学学院）；School of Engineering and IT, University of New South Wales（新南威尔士大学工程与信息技术学院）；Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Huayin Institute of Technology（淮阴工学院）
Understanding the evolutionary patterns of real-world evolving complex systems such as human interactions, transport networks, biological interactions, and computer networks has important implications in our daily lives. Predicting future links among the nodes in such networks reveals an important aspect of the evolution of temporal networks. To analyse networks, they are mapped to adjacency matrices, however, a single adjacency matrix cannot represent complex relationships (e.g. temporal pattern), and therefore, some approaches consider a simplified representation of temporal networks but in high-dimensional and generally sparse matrices. As a result, adjacency matrices cannot be directly used by machine learning models for making network or node level predictions. To overcome this problem, automated frameworks are proposed for learning low-dimensional vectors for nodes or edges, as state-of-the-art techniques in predicting temporal patterns in networks such as link prediction. However, these models fail to consider temporal dimensions of the networks. This gap motivated us to propose in this research a new node embedding technique which exploits the evolving nature of the networks considering a simple three-layer graph neural network at each time step, and extracting node orientation by Given's angle method. To prove our proposed algorithm's efficiency, we evaluated the efficiency of our proposed algorithm against six state-of-the-art benchmark network embedding models, on four real temporal networks data, and the results show our model outperforms other methods in predicting future links in temporal networks.
A Preference Random Walk Algorithm for Link Prediction through Mutual Influence Nodes in Complex Networks
Queensland University of Technology（昆士兰科技大学）；Azarbaijan Shahid Madani University（阿塞拜疆沙希德·马达尼大学）；University of Applied Science and Technology（应用科学与技术大学）
Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research Asia（微软亚洲研究院）
Link prediction on multi-relational graphs from an influence propagation perspective
School of Information Science and Technology, Yunnan Normal University（云南师范大学信息科学与技术学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Computer Science and Engineering, The University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；School of Computer and Mathematical Sciences, Adelaide University（阿德莱德大学计算机与数学科学学院）
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology（中国矿业大学计算机科学与技术学院/人工智能学院）；Mine Digitization Engineering Research Center of the Ministry of Education（教育部矿山数字化工程研究中心）；Jiangsu Provincial Industrial Technology Engineering Center for Intelligent Sensing and Emergency IoT in Underground Space（江苏省地下空间智能传感与应急物联网产业技术工程中心）；School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气工程与计算机科学学院）
IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
Dankook University（檀国大学）；Yonsei University（延世大学）；Posts and Telecommunications Institute of Technology（邮电技术学院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；University of Konstanz（康斯坦茨大学）
Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Binzhou Institute of Technology（滨州职业技术学院）
