# Network Repository

Note: Graphs in this repository are tagged with their real-world source, have in-depth analysis, and even a preview visualization of each individual graph. Types of graphs include social networks, infrastructure networks, biological networks, and many others. The repository also offers sources for individual graphs. It contains many graphs from other benchmark sets described here.
Origin Notes: This repository was proposed in 2015 by Rossi and Ahmed of Purdue University as a visually interactive real world graph tool and database.
graph features handled: Categorical nodes, Directed edges, Dynamic, Generic, Labeled nodes, Large, Sparse, Spatial, Weighted edges, temporal
Graph features in papers: generic,large
Origin Paper: The Network Data Repository with Interactive Graph Analytics and Visualization (https://www.notion.so/The-Network-Data-Repository-with-Interactive-Graph-Analytics-and-Visualization-0f75154d4bf64f4b8a3c13dbddd671d4?pvs=21)
Originally found at: https://networkrepository.com/
Number of Graphs: 6659
Appeared in years: 2017,2016
Type of Collection: Established Network Repo (No report)
is it stored properly?: No
must be analyzed: No
In repo?: No
Related to Literature - Algorithm (1) (Dataset tag relations): Drawing Big Graphs Using Spectral
Sparsification (https://www.notion.so/Drawing-Big-Graphs-Using-Spectral-Sparsification-89da2043d0444f75a6c663b1fc999512?pvs=21), A Distributed Multilevel Force-Directed Algorithm (https://www.notion.so/A-Distributed-Multilevel-Force-Directed-Algorithm-9d7a1734402948ecba4995ca617489ff?pvs=21)
cleaned format?: No
duplicate?: No
link works?: No
Added in paper: Yes
Origin paper plaintext: The Network Data Repository with Interactive Graph Analytics and Visualization
Page id: 8d5eb290b4274266986c4d4e98c7dc34
unavailable/skip: Yes
Cleaned ALL data: No
first look: No
Related to Literature - Algorithm (Dataset tag relations) 1: Drawing Big Graphs Using Spectral
Sparsification (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Drawing%20Big%20Graphs%20Using%20Spectral%20Sparsification%20e5d3efdca48541f2b1789ec74357ebf6.md), A Distributed Multilevel Force-Directed Algorithm (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/A%20Distributed%20Multilevel%20Force-Directed%20Algorithm%200a5a2af21adb4edbbc41684e61b3db32.md)

# Body

### Descriptions from Literature

From “[A Distributed Multilevel Force-Directed Algorithm](https://doi.org/10.1109/TPDS.2018.2869805)”:

> To evaluate the running time of MULTI-GILA, we used the REALGRAPHS and BIGGRAPHS benchmarks, which contain larger graphs extracted from both real-world applications and synthetic generators; see Table 4. The REALGRAPHS set includes the 5 largest real-world graphs (mainly scale-free graphs) used in the experimental study of GILA [5]. These graphs are taken from the Stanford Large Networks Dataset Collection [3] and from the **Network Data Repository** [46], and their number of edges is between 121523 and 1541514.
> 

From “[Drawing Big Graphs Using Spectral Sparsification](https://link.springer.com/chapter/10.1007/978-3-319-73915-1_22)”:

> We used three data sets. The first set of graphs is taken from “defacto-benchmark” graphs, including the Hachul library, Walshaw’s Graph Partitioning Archive, the sparse matrices collection [[6](https://link.springer.com/chapter/10.1007/978-3-319-73915-1_22#ref-CR6)] and the network repository [[24](https://link.springer.com/chapter/10.1007/978-3-319-73915-1_22#ref-CR24)]. These include two types of graphs that have been extensively studied in graph drawing research: grid-like graphs and scale-free graphs.
> 

### Example Figures