UCINET(University of California of Irvine Network Programms)如其他的網絡分析軟體，它處理的原始Data Set必須是由研究者所coding的actor-actor或actor-issue 矩陣資料，透過個人與個人或個人與事件間的"關係連結"，電腦乃能辨識其處理的分析單位，並且透過不同的指令來作不同的分析。社會網路分析方法包括centrality 測量，分小組鑑定，角色分析，初步圖論和基於換變的統計分析。

會網路分析方法包括centrality 測量，分小組鑑定，角色分析，初步圖論和基於換

系統需求

• Windows operating system NT, 98, XP, Vista, Win 7, and (we assume) Win 8. If you have a Mac or Linux, you can run UCINET via BootCamp, VMFusion Ware, Parallels or Wine. See our on this.
• The 32-bit version is the standard one and runs on both 32bit and 64bit Windows systems. An experimental 64-bit version is available if want to try it out
• 100mb of disk space for the program itself (not including your data)
• The more RAM the better, but the 32-bit version can't take advantage of more than 3GB of memory. If you have large data and a 64-bit version of Windows, you can try experimental 64-bit version, in which case 8GB of RAM or more would be useful. Remember, however, that even if a really large dataset fits in memory, it may take too long to analyze.
• While the absolute maximum network size is about 2 million nodes, in practice most UCINET procedures are too slow to run networks larger than about 5000 nodes. However, this varies depending on the specific analysis and the sparseness of the network. For example, degree centrality can be run on networks of tens of thousands of nodes, and most graph theoretic routines run faster when you have very few ties, no matter how many nodes you have.