reddit_graphsage — Reddit posts linked by common commenters (GraphSAGE, 2014)

Description

Networks of the posts made on Reddit in September 2014, two posts being connected if the same user commented on both, built by W. L. Hamilton, R. Ying and J. Leskovec as a benchmark for the inductive node classification method GraphSAGE, from the Reddit data of Pushshift. The posts are those of 50 large communities (subreddits), and the task of the benchmark is to predict the community of a post. The first 20 days of the month were used for training, and the rest for validation (30%) and testing (70%).

There are two networks, both distributed with GraphSAGE:

full: the complete network (reddit-G_full.json), with all 232,965 posts and 57.3 million edges (average degree 492, as given in the paper). This is also the network of the Reddit dataset of the libraries DGL and PyTorch Geometric (whose copy, with the nodes in another order, has the same numbers of nodes and edges).
graphsage: the sparser network distributed as the main graph of the benchmark (reddit-G.json), with 231,443 posts and 11.6 million edges, which is also the network of the Reddit2 dataset of PyTorch Geometric (from GraphSAINT). How it was obtained from the full network is not documented.

The communities are given only as numbered classes (41 of them appear), not by name. Each post also has the node features of the benchmark: a 602-dimensional vector made of the average of the 300-dimensional GloVe word embeddings of the title of the post, the average of those of its comments, its score and its number of comments (the dimensions are as given by the source; their order is not documented).

The networks have the following properties:

post_id: the identifier of the post on Reddit (in base 36, e.g. 2hgylt).
label: the class of the community of the post (a number from 0 to 40).
split: the set of the post in the benchmark: train, val or test.
features: the 602-dimensional feature vector of the post.

Tags

Social Online Projection Unweighted Metadata

Citation

W. L. Hamilton, R. Ying, J. Leskovec, "Inductive Representation Learning on Large Graphs." Advances in Neural Information Processing Systems 30, 1024-1034 (2017) 10.48550/arXiv.1706.02216 Sci-Hub

Upstream URL OK

https://snap.stanford.edu/graphsage/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
full	232,965	57,307,946	491.99	799.82	2358.81	50.36	0.11	0.24	8	1.00	Undirected	Unipartite	post_id label split features		702.4 MiB	1.301 GiB	1.286 GiB	1.036 GiB
graphsage	231,443	11,606,919	100.30	125.55	348.72	72.61	0.10	0.05	10	1.00	Undirected	Unipartite	post_id label split features		624.3 MiB	1.112 GiB	1.112 GiB	896.2 MiB