Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital w...Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.展开更多
对一般三角网生长法做了简要介绍和分析,针对限制算法效率提高的关键步骤——"搜索符合条件的第三点",提出了一种"第三点分区搜索法"的改进算法。通过一系列的圆弧将离散点区域划分成多个分区,构网时规定只可在当...对一般三角网生长法做了简要介绍和分析,针对限制算法效率提高的关键步骤——"搜索符合条件的第三点",提出了一种"第三点分区搜索法"的改进算法。通过一系列的圆弧将离散点区域划分成多个分区,构网时规定只可在当前分区和相邻的下一分区搜索第三点,当该分区的离散点搜索完毕后进入下一分区。在Microsoft Visual Studio 2008的环境下使用C++进行编程测试,结果表明,该算法能够加快构网速度,生成的三角形形状良好,具有一定的实际效用。展开更多
文摘Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.
文摘对一般三角网生长法做了简要介绍和分析,针对限制算法效率提高的关键步骤——"搜索符合条件的第三点",提出了一种"第三点分区搜索法"的改进算法。通过一系列的圆弧将离散点区域划分成多个分区,构网时规定只可在当前分区和相邻的下一分区搜索第三点,当该分区的离散点搜索完毕后进入下一分区。在Microsoft Visual Studio 2008的环境下使用C++进行编程测试,结果表明,该算法能够加快构网速度,生成的三角形形状良好,具有一定的实际效用。
