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. Jure Leskovec and Rok Sosič. 2016. SNAP: A general-purpose network analysis and graph-mining library. Network analysis tools are expected to fulfill a set of requirements. They need to provide rich functionality, implementing a wide range of graph and network analysis algorithms. Implementations of graph algorithms must be able to process graphs with hundredss of millions of nodes. Graphs need to be represented in a compact form with a small memory footprint, since many algorithms are bound by the memory throughput. Powerful operators are required for modifying graph structure, so that nodes and edges in a graph can be added or removed, or new graphs can be constructed from existing ones. Additionally, for a wide system adoption, it is desirable that the source code is available under an open-source license. While there has been a significant amount of work on systems for processing and analyzing large graphs, none of the existing systems fulfills the requirements outlined earlier. In particular, research on graph processing in large-scale distributed environments [Gonzalez et al. 2012; Malewicz et al. 2010; Kang et al. 2009; Salihoglu and Widom 2013; Xin et al. 2013 ] provides efficient frameworks, but these frameworks implement only a handful of most common graph algorithms, which, in practice, is not enough to make these tools useful for practitioners. Similarly, there are several userfriendly libraries that implement dozens of network analysis algorithms [Batagelj and Mrvar 1998; Csardi and Nepusz 2006; Gregor and Lumsdaine 2005; Hagberg et al. 2008; O'Madadhain et al. 2005 ]. However, the limitations of these systems are that they might not scale to large graphs, can be slow, hard to use, or do not include support for dynamic networks. Thus, there is a need for a system that addresses those limitations and provides reasonable scalability, is easy to use, implements numerous graph algorithms, and supports dynamic networks. Here, we present the Stanford Network Analysis Platform (SNAP), which was specifically built with these requirements in mind. SNAP is a general-purpose, highperformance system that provides easy-to-use, high-level operations for analysis and manipulation of large networks. SNAP has been developed for single big-memory multiple-core machines; as such, 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 offers methods that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that network structure and attributes are fully dynamic; they can be modified during the computation via low-cost operations. Overall, SNAP implements 8 graph and network types, 20 graph generation methods/models, 20 graph manipulation methods, and over 100 graph algorithms, which provides in total over 200 different functions. It has been used in a wide range of applications, such as network inference [Gomez-Rodriguez et al. 2014 ], network optimization [Hallac et al. 2015], information diffusion [Leskovec et al. 2009; Suen et al. 2013], community detection [Yang and Leskovec 2014], and geospatial network analysis [Leskovec and Horvitz 2014]. SNAP is provided for major operating systems as an open-source library in C++ as well as a module in Python. It is released under the BSD open-source license and can be downloaded from http://snap.stanford.edu/ snap. Complementary to the SNAP software, we also maintain the public Stanford Large Network Dataset Collection, an extensive set of social and information networks with about 80 different network datasets. The collection includes online social networks with rich dynamics and node attributes, communication networks, scientific citation networks, collaboration networks, web graphs, Internet networks, online reviews, as well as social-media data. The network datasets can be obtained at http://snap. stanford.edu/data.
SNA software tools include the Stanford Network Analysis Platform REF and the Social Networks Visualizer (Kalamaras) .
SNAP: A General-Purpose Network Analysis and Graph-Mining Library
TIST
Computer Science Physics Medicine