js-pert 计划评审技术 给定一系列具有悲观，乐观，可能的时间和依赖性的活动，这些活动将提供： 每次活动的expected time 每项活动的variance 具有predecessors和successors节点[AON]网络图上活动的描述 每个节点的最早开始[ES]次 每个节点最早完成[EF]次 每个节点的最晚开始[LS]次 每个节点的最新完成[LF]次 每个节点都slack critical path描述 使用pert的描述还将提供一个函数，以计算x天完成项目的概率。 安装 npm install js-pert --save 例 请看。 文献资料 jsPERT 默认的导

SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Jusin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that "go viral" and result in millions of impressions. O'Reilly's "Mining Social Media" and "Programming Collective Intelligence" books are an excellent start for people inteseted in SNA. This book builds on these books' foundations to teach a new, pragmatic, way of doing SNA. I would like to write a book that links theory ("why is this important?", "how do various concepts interact?", "how do I interpret quantitative results?") and practice -- gathering, analyzing and visualizing data using Python and other open-source tools.

计算机网络教材:Morgan.Kaufmann.Network.Analysis.Architecture.and.Design.3rd.Edition.Jun.2007.pdf

Practical Social Network Analysis with Python英文原版，内含pdf和epub两种格式。Springer出版社出版。

This book covers construction, exploration, analysis, and visualization of complex networks using NetworkX (a Python library), as well as several other Python modules, and Gephi, an interactive environment for network analysts. The book is not an introduction to Python. I assume that you already know the language, at least at the level of a freshman programming course. The book consists of five parts, each covering specific aspects of complex networks. Each part comes with one or more detailed case studies. Part I presents an overview of the main Python CNA modules: NetworkX, iGraph, graph-tool, and networkit. It then goes over the construction of very simple networks both programmatically (using NetworkX) and interactively (in Gephi), and it concludes by presenting a network of Wikipedia pages related to complex networks. In Part II, you’ll look into networks based on explicit relationships (such as social networks and communication networks). This part addresses advanced network construction and measurement techniques. The capstone case study—a network of “Panama papers”—illustrates possible money-laundering patterns in Central Asia. Networks based on spatial and temporal co-occurrences—such as semantic and product networks—are the subject of Part III. The third part also explores macroscopic and mesoscopic complex network structure. It paves the way to network-based cultural domain analysis and a marketing study of Sephora cosmetic products. If you cannot find any direct or indirect relationships between the items, but still would like to build a network of them, the contents of Part IV come to the rescue. You will learn how to find out if items are similar, and you will convert quantitative similarities into network edges. A network of psychological trauma types is one of the outcomes of the fourth part. The book concludes with Part V: directed networks with plenty of examples, including a network of qualitative adjectives that you could use in computer games or

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s resea

本书是“Introduction to Network Analysis” 一书的续集，是针对那些网络工作者并且需要对网 络进行排错和优化进行设计并提供支持的。

Exploratory+Social+Network+Analysis+With+Pajek.pdf 基于pajek分析社会网络专著

目前国外最先进的脑网络分析基础书籍，很多先进的分析方法都在其中，国内无法下载，国外顶尖高校才能拿到的资源。这本书是英文版的，非常权威。中文翻译版的还没出，日后出了我会传上来。