dttutty/frost_dataset

FROST DGB Temporal Graph Datasets

This repository packages the curated temporal graph datasets used by FROST as a flat artifact bundle and is published on Hugging Face as dttutty/frost_dataset. Each top-level dataset directory stores the canonical preprocessed edge list plus any optional sidecar arrays needed for downstream experiments. The layout is intended for exact file download with hf download, snapshot_download(), or hf_hub_download(), not for automatic datasets.load_dataset() ingestion or a Viewer-first tabular experience.

Download From Hugging Face

Use the dataset repository as a file bundle:

# Full bundle
hf download dttutty/frost_dataset --repo-type dataset --local-dir DATA

# One dataset directory only
hf download dttutty/frost_dataset --repo-type dataset --include "LASTFM/*" --local-dir DATA

If you do not want to install hf globally, uvx --from huggingface_hub hf ... works the same way. --local-dir DATA mirrors the repository tree into DATA/ and creates DATA/.cache/huggingface/ metadata for incremental refreshes.

Repository Layout

• <dataset>/edges.csv is the canonical preprocessed edge table.
• <dataset>/full_graph_with_reverse_edges.npz is the topology cache used directly by FROST runtime loading.
• <dataset>/edge_features.npy is present when the dataset exposes non-zero edge features. Some datasets keep the original float tensor; selected integer-valued datasets use a downcast materialized copy.
• <dataset>/ban_labels.csv exists for MOOC, REDDIT, and WIKIPEDIA, preserving the non-trivial user-state label as a separate sidecar table.
• <dataset>/node_role.npy is included for selected bipartite datasets that need node-role annotations.

Use From Python

Use this repository as a file bundle:

from pathlib import Path

from huggingface_hub import snapshot_download

repo_dir = Path(
    snapshot_download(
        repo_id="dttutty/frost_dataset",
        repo_type="dataset",
        local_dir="DATA",
    )
)

edges = repo_dir / "MOOC" / "edges.csv"
graph = repo_dir / "MOOC" / "full_graph_with_reverse_edges.npz"
edge_features = repo_dir / "MOOC" / "edge_features.npy"
labels = repo_dir / "MOOC" / "ban_labels.csv"

If you need a datasets library dataset or a Hub Viewer-backed table, export the per-dataset artifacts into a supported viewer-first layout such as CSV or Parquet with explicit split or config metadata. The current repository mixes CSV, NPY, and NPZ sidecar files and is optimized for artifact download instead of viewer-native browsing.

Format Notes

• Upstream raw DGB networks are originally stored as <dataset>.csv, with one edge per line.
• The raw edge-list schema is source_node,destination_node,timestamp,edge_label,edge_features....
• This bundle keeps the baseline-friendly preprocessed files directly under each dataset directory.
• edges.csv stores the preprocessed event table with columns eid, src, dst, ts, and default_split.
• The original constant or task-specific label column has been removed from the top-level edges.csv files. When a non-trivial state label is useful downstream, it is preserved separately in ban_labels.csv.
• edge_features.npy stores the dense edge-feature matrix when the dataset contains non-zero edge features.
• ban_labels.csv stores the extracted state-label sidecar for MOOC, REDDIT, and WIKIPEDIA. The filename is historical; for MOOC it still contains dropout-style state labels rather than ban labels.
• node_role.npy stores a boolean bipartite partition mask when the dataset needs it.
• Preprocessed .npy files often have one extra leading row for index alignment or padding, so their first dimension is usually edge_count + 1 or node_count + 1.

state_label / label Notes

• In DGB or DyGLib preprocessing, the preprocessed CSV can carry a label column copied from the raw state_label field.
• This repository preserves that signal only where it is non-trivial, via top-level ban_labels.csv for MOOC, REDDIT, and WIKIPEDIA.
• MOOC: state(label) means whether the student drops out after this action, that is, whether this is the user's last action. In this repository: 1 = 4,066, 0 = 407,683.
• Wikipedia: state(label) is the ban-state label, that is, whether the user gets banned after this action. In this repository: 1 = 217, 0 = 157,257.
• Reddit: state(label) is the user state-change label; on Reddit this specifically means whether the user gets banned after this interaction. In this repository: 1 = 366, 0 = 672,081.
• SocialEvo: the original state(label) is degenerate and always 1.
• The other top-level datasets in this bundle have a constant source label and therefore do not carry a separate label sidecar.
• In the self-supervised link prediction pipelines used by DGB and DyGLib, these stored state(label) values are not used as link-prediction targets; positive and negative labels are created on the fly from observed edges and sampled negative edges.
• JODIE's original state-change setting does use these labels for user-state prediction tasks such as MOOC dropout prediction and Wikipedia or Reddit ban prediction.

Normalization Notes

• The published bundle already includes the normalization that FROST expects at runtime.
• CanParl, UNtrade, and UNvote use contiguous 0-based yearly timestamp indices.
• Selected integer-valued edge-feature arrays are materialized with lossless downcasts for storage efficiency.
• MOOC, REDDIT, and WIKIPEDIA expose their non-trivial state labels as top-level ban_labels.csv sidecars.

Recommended max_batch_size

These conservative recommendations are derived from the num_edges values below, assuming the standard 80/10/10 train/val/test split and:

max_batch_size = floor(num_edges / (10 * N_GPU))

This keeps batch_size * N_GPU within the approximate smallest evaluation split budget for the current FROST runtime.

Dataset	N_GPU=1	N_GPU=2	N_GPU=4	N_GPU=8
CanParl	7447	3723	1861	930
Contacts	242627	121313	60656	30328
Flights	192714	96357	48178	24089
SocialEvo	209951	104975	52487	26243
UNtrade	50749	25374	12687	6343
UNvote	103574	51787	25893	12946
USLegis	6039	3019	1509	754
Enron	12523	6261	3130	1565
LastFM	129310	64655	32327	16163
MOOC	41174	20587	10293	5146
Reddit	67244	33622	16811	8405
UCI	5983	2991	1495	747
Wikipedia	15747	7873	3936	1968

Dataset Details

• Source or destination ranges are computed from */edges.csv (u, i).
• The curated bundle already reflects timestamp normalization for CanParl, UNtrade, and UNvote, downcast integer edge-feature materialization for CanParl, Contacts, Flights, UNtrade, UNvote, and USLegis, and state-label extraction into ban_labels.csv for MOOC, REDDIT, and WIKIPEDIA.
• DGB paper: https://arxiv.org/pdf/2207.10128
• Features & Labels lists only non-edges.csv sidecar artifacts, shown as size, rowsxcols, dtype.

Dataset	SRC_NID	DST_NID	Notes	Features & Labels	TS INFO
CanParl (num_edges: 74,478)	range: 1->734 unique: 734	range: 2->734 unique: 244	Canadian MP interaction network. Edge weight = yearly count of shared "yes" votes on bills.	edge_features.npy 145.6KB, 74,479x1, int16	yearly range=[0->13] unique=14
Contacts (num_edges: 2,426,279)	range: 1->692 unique: 676	range: 1->690 unique: 676	University-student physical proximity network over one month. Edge weight = proximity strength.	edge_features.npy 2.3MB, 2,426,280x1, int8	Second range=[0->2418900] unique=8064
Flights (num_edges: 1,927,145)	range: 1->13169 unique: 11574	range: 1->13169 unique: 12939	Airport traffic during COVID-19. Edge weight = number of flights between two airports in a day.	edge_features.npy 3.7MB, 1,927,146x1, int16	daily range=[0->121] unique=122
SocialEvo (num_edges: 2,099,519)	range: 1->74 unique: 74	range: 1->74 unique: 70	Mobile phone proximity network in an undergraduate dorm over eight months. Each edge has a 2-dim feature.	edge_features.npy 32.0MB, 2,099,520x2, float64	Second range=[0->20,935,623] unique=565,932
UNtrade (num_edges: 507,497)	range: 1->255 unique: 255	range: 1->255 unique: 254	Food and agriculture trade between nations over 30+ years. Edge weight = normalized import or export value.	edge_features.npy 1.9MB, 507,498x1, int32	yearly range=[0->31] unique=32
UNvote (num_edges: 1,035,742)	range: 1->201 unique: 201	range: 1->201 unique: 201	UN General Assembly roll-call votes. Edge weight increases when two nations both vote "yes".	edge_features.npy 2.0MB, 1,035,743x1, int16	yearly range=[0->71] unique=72
USLegis (num_edges: 60,396)	range: 1->225 unique: 224	range: 1->225 unique: 225	US Senate co-sponsorship network. Edge weight = number of shared bill co-sponsorships in a congress.	edge_features.npy 118.1KB, 60,397x1, int16	bi-yearly range=[0->11] unique=12
Enron (num_edges: 125,235)	range: 1->184 unique: 181	range: 1->184 unique: 184	Email communications between Enron employees over three years.	n/a	Second range=[0->113,740,399] unique=22,632
LastFM (num_edges: 1,293,103)	range: 1->980 unique: 980	range: 981->1980 unique: 1000	Bipartite user-song listening graph over one month.	node_role.npy 2.1KB, 1,981x1, bool	Second range=[0->137,107,267] unique=1,283,614
MOOC (num_edges: 411,749)	range: 1->7047 unique: 7047	range: 7048->7144 unique: 97	Bipartite student-content interaction graph. Each edge has a 4-dim feature.	edge_features.npy 12.6MB, 411,750x4, float64 ban_labels.csv 2.0MB, 411,749x1, bool node_role.npy 7.1KB, 7,145x1, bool	Second range=[0->2,572,086] unique=345,600
Reddit (num_edges: 672,447)	range: 1->10000 unique: 10000	range: 10001->10984 unique: 984	Bipartite user-subreddit posting graph over one month. 172-dim LIWC edge feature. Dynamic ban labels.	edge_features.npy 882.4MB, 672,448x172, float64 ban_labels.csv 3.3MB, 672,447x1, bool node_role.npy 10.9KB, 10,985x1, bool	Millisecond range=[0->2,678,390,016] unique=669,065
UCI (num_edges: 59,835)	range: 1->1899 unique: 1350	range: 1->1898 unique: 1862	Online communication network where nodes are university students. Edges are posted messages.	n/a	Second range=[0->16,736,181] unique=58,911
Wikipedia (num_edges: 157,474)	range: 1->8227 unique: 8227	range: 8228->9227 unique: 1000	Bipartite user-page editing graph over one month. 172-dim LIWC edge feature. Dynamic temporary-ban labels.	edge_features.npy 206.6MB, 157,475x172, float64 ban_labels.csv 772KB, 157,474x1, bool node_role.npy 9.1KB, 9,228x1, bool	Second range=[0->2,678,373] unique=152,757