Social GAN——可视化-附件资源

social gan model and dataset for free to download

FlaskBB FlaskBB是使用微型框架Flask用Python编写的论坛软件。 当前，实现了以下功能： 私人信息 管理界面 基于组的权限 降价支持 主题追踪器 未读的主题/论坛 i18n支持 完全主题化 插件系统 命令行界面 由FlaskBB支持的官方论坛可以通过进行。 快速开始 有关完整的安装指南，请访问的安装文档。 这是您设置FlaskBB的开发实例的方式： 创建一个virtualenv 组态make devconfig 安装依赖和FlaskBB make install 运行开发服务器make run 访问 执照 FlaskBB已获得。 链接

模拟行人从房间疏散过程，基本原理采用社会力模型

smo优化优化matlab代码SMO 社交拟态优化算法及工程应用 社会拟态优化算法和工程应用的示例 Matlab 代码（0.1 版） 您只需要在 Costfunc.m 中引入要最小化的函数。 然后输入 SMO 参数和问题参数（第 28 至 40 行）。 请在您的研究论文中参考以下期刊文章：Saeed Balochian、Hossein Baloochian，Social Mimic Optimization Algorithm and Engineering Applications，Expert Systems with Applications，2019，ISSN 0957-4174。 ()。 社会模仿优化算法及工程应用 MATLAB 代码示例（0.1 版） 优化社会模仿和工程应用的算法，将成本函数写入文件（Costfunc.m）中，仅供使用。 然后输入算法参数如人口数和问题参数如变量数和取值范围（第28至40行） 请在您的研究文章中参考以下期刊文章：()。

social and economic network M.O Jackson chapter1

People increasingly use social networks to manage various aspects of their lives such as communication, collaboration, and information sharing. A user’s network of friends may offer a wide range of important benefits such as receiving online help and support and the ability to exploit professional opportunities. One of the most profound properties of social networks is their dynamic nature governed by people constantly joining and leaving the social networks. The circle of friends may frequently change when people establish friendship through social links or when their interest in a social relationship ends and the link is removed. This book introduces novel techniques and algorithms for social network-based recommender systems. Here, concepts such as link prediction using graph patterns, following recommendation based on user authority, strategic partner selection in collaborative systems, and network formation based on “social brokers” are presented. In this book, well-established graph models such as the notion of hubs and authorities provide the basis for authority-based recommendation and are systematically extended towards a unified Hyperlink Induced Topic Search (HITS) and personalized PageRank model. Detailed experiments using various real-world datasets and systematic evaluation of recommendation results proof the applicability of the presented concepts.

动态图表示学习，动态图分析论文汇总项目 本项目总结了动态图表示学习的有关论文，该项目在持续更新中，欢迎大家看/星/叉！ 如果大家有值得推荐的工作，可以在问题中提出要推荐的工作，论文下载链接及其工作亮点（有优秀代码实现的工作，会优先考虑在内）。项目中表述有误的部分，也可以在issue中提出。感谢！ 引流：【这也是我们的工作，欢迎手表/星/叉】 社交知识图谱专题： : 目录如下： 静态图表示与分析工作 针对静态图表示学习以及静态图分析，挖掘领域，挑选出个人认为值得继承的引用数更高，知名度较高的或最近的一些工作。 node2vec：网络的可扩展功能学习 作者：Grover A，Leskovec J.（阿姆斯特丹大学） 发表时间：2016 发表于：KDD 2016 标签：图表示学习 概述：依据表示学习，提出了一套在网络中学习连续连续类型表示的方法，取代了传统使用人工定义的例程结构化特征的方式

社会力量模型行人模拟 我的论文工作的仿真程序的源代码。 我在20多年前编写了这段代码。 当C ++变得流行时...可用于DEC Ultrix。 也许其中一些有用。

Does your startup rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available. Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you'll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You'll also learn how to use Python and other open source tools—such as NetworkX, NumPy, and Matplotlib—to gather, analyze, and visualize social data. This book is the perfect marriage between social network theory and practice, and a valuable source of insight and ideas. Discover how internal social networks affect a company’s ability to perform Follow terrorists and revolutionaries through the 1998 Khobar Towers bombing, the 9/11 attacks, and the Egyptian uprising Learn how a single special-interest group can control the outcome of a national election Examine relationships between companies through investment networks and shared boards of directors Delve into the anatomy of cultural fads and trends—offline phenomena often mediated by Twitter and Facebook