Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social-network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe the Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy-to-use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines, and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs in which nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and metadata on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic; they can be modified during the computation at low cost. SNAP is provided as an open-source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.

中文翻译：

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折断

大型网络正在成为广泛使用的抽象概念，用于研究广泛学科中的复杂系统，范围从社会网络分析到分子生物学和神经科学。尽管分析和操作大型网络的需求日益增加，但只有有限数量的工具可用于此任务。在这里，我们描述了斯坦福网络分析平台 (SNAP)，这是一个通用的高性能系统，为大型网络的分析和操作提供易于使用的高级操作。我们展示了 SNAP 功能，描述了它的实现细节，并给出了性能基准。SNAP 是为单个大内存机器开发的，它平衡了最大性能、紧凑的内存图表示、以及处理随时间添加或删除节点和边的动态图的能力。SNAP 可以处理具有数亿个节点和数十亿条边的海量网络。SNAP 提供超过 140 种不同的图算法，可以有效地操作大型图、计算结构属性、生成规则和随机图，以及处理节点和边上的属性和元数据。除了能够处理大图之外，SNAP 的另一个优势是网络及其属性是完全动态的。它们可以在计算过程中以低成本进行修改。SNAP 作为 C++ 中的开源库和 Python 中的模块提供。我们还描述了斯坦福大型网络数据集，这是一组我们公开提供的社交和信息真实世界网络和数据集。