Add FROST datasets snapshot

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CanParl/processed_data/edges.csv

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Flights/processed_data/edges.csv

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README.md

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+# DATA Inventory
+
+Scanned from the current checkout on 2026-03-26 UTC. This document focuses on the DGB datasets currently kept under `DATA/`.
+
+- `DATA/.temp/` is the temporary staging area used by `scripts/download_dgb_data.sh`.
+- `DATA/DGB_TG_network_datasets/raw_data/` is a mirror bundle of the same DGB datasets already unpacked into the individual dataset directories below; zero-only `.npy` files were pruned from both the top-level dataset folders and this mirror bundle, so it is not expanded again here.
+
+## Format Overview
+
+- 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...`.
+- When a dataset has no edge label or no features, the baseline preprocessing may emit zero-filled placeholders for loading compatibility; this checkout later prunes all-zero `.npy` tensors instead of keeping them around.
+- `ml_<dataset>.csv`: baseline-friendly preprocessed edge list, typically `u,i,ts,label,idx`.
+- `ml_<dataset>.npy`: dense edge-feature matrix for the preprocessed edge list when it contains non-zero information.
+- `ml_<dataset>_node.npy`: dense node-feature matrix when it contains non-zero information.
+- `processed_data/edges.csv`: symlink to `raw_data/ml_<dataset>.csv`.
+- `processed_data/edge_feature.npy`: symlink to `raw_data/ml_<dataset>.npy` when the edge-feature tensor exists and was not pruned as all zero, or a materialized downcast copy for selected integer-valued datasets.
+- When both `<dataset>.csv` and `ml_<dataset>.csv` are present in the download, this checkout removes the redundant original `<dataset>.csv` and keeps the preprocessed `ml_<dataset>.csv`.
+- In this checkout, the preprocessed `.npy` files often have one extra leading row for index alignment/padding, so their first dimension is usually `edge_count + 1` or `node_count + 1`.
+
+## `state_label` / `label` Notes
+
+- In DGB/DyGLib preprocessing, `ml_<dataset>.csv.label` is copied from the raw `state_label` column when the raw CSV is available.
+- `scripts/download_dgb_data.py` additionally extracts `processed_data/dynamic_node_labels.csv` from the second-to-last `label` column of `ml_mooc.csv`, `ml_reddit.csv`, and `ml_wikipedia.csv`.
+- `MOOC`: `state(label)` means whether the student drops out after this action, i.e. whether this is the user's last action. In this checkout: `1 = 4,066`, `0 = 407,683`.
+- `Wikipedia`: `state(label)` is the ban-state label, i.e. whether the user gets banned after this action. In this checkout: `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 checkout: `1 = 366`, `0 = 672,081`.
+- `SocialEvo`: `state(label)` is degenerate in this checkout and is always `1` for every row.
+- Other top-level datasets in this checkout have `state(label)` always equal to `0`.
+- 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/Reddit ban prediction.
+
+## Dataset Details
+
+- Source/destination ranges are computed from `DATA/DGB_*/raw_data/ml_*.csv` (`u`, `i`).
+- `scripts/download_dgb_data.py` removes all-zero raw `*.npy` tensors, normalizes `CanParl`, `UNtrade`, and `UNvote` yearly timestamps to 0-based indices, downcasts `processed_data/edge_feature.npy` for `CanParl`, `Contacts`, `Flights`, `UNtrade`, `UNvote`, and `USLegis`, extracts `processed_data/dynamic_node_labels.csv` for `MOOC`, `Reddit`, and `Wikipedia`, and removes redundant non-`ml_*.csv` raw CSVs after the preprocessed `ml_*.csv` files are in place.
+- DGB paper: [https://arxiv.org/pdf/2207.10128](https://arxiv.org/pdf/2207.10128)
+- `Features & Labels` lists only non-`edges.csv` processed artifacts, shown as `size, rows×cols, dtype`.
+
+| Dataset | SRC_NID | DST_NID | Notes | Features & Labels | TS INFO |
+| --- | --- | --- | --- | --- | --- |
+| [CanParl](DGB_CanParl/processed_data/edges.csv) (num_edges: 74,478) | range: 1->734<br>unique: 734 | range: 2->734<br>unique: 244 | Canadian MP interaction network.<br>Edge weight = yearly count of shared "yes" votes on bills. | [edge_feature.npy](DGB_CanParl/processed_data/edge_feature.npy) 145.6KB, 74,479×1, int16 | yearly<br>range=[0->13]<br>unique=14 |
+| [Contacts](DGB_Contacts/processed_data/edges.csv) (num_edges: 2,426,279) | range: 1->692<br>unique: 676 | range: 1->690<br>unique: 676 | University-student physical proximity network over one month.<br>Edge weight = proximity strength. | [edge_feature.npy](DGB_Contacts/processed_data/edge_feature.npy) 2.3MB, 2,426,280×1, int8 | n/a |
+| [Flights](DGB_Flights/processed_data/edges.csv) (num_edges: 1,927,145) | range: 1->13169<br>unique: 11574 | range: 1->13169<br>unique: 12939 | Airport traffic during COVID-19.<br>Edge weight = number of flights between two airports in a day. | [edge_feature.npy](DGB_Flights/processed_data/edge_feature.npy) 3.7MB, 1,927,146×1, int16 | daily<br>range=[0->121]<br>unique=122 |
+| [SocialEvo](DGB_SocialEvo/processed_data/edges.csv) (num_edges: 2,099,519) | range: 1->74<br>unique: 74 | range: 1->74<br>unique: 70 | Mobile phone proximity network in an undergraduate dorm over eight months.<br>Each edge has a 2-dim feature. | [edge_feature.npy](DGB_SocialEvo/processed_data/edge_feature.npy) 32.0MB, 2,099,520×2, float64 | Second<br>range=[0->20,935,623]<br>unique=565,932 |
+| [UNtrade](DGB_UNtrade/processed_data/edges.csv) (num_edges: 507,497) | range: 1->255<br>unique: 255 | range: 1->255<br>unique: 254 | Food and agriculture trade between nations over 30+ years.<br>Edge weight = normalized import/export value. | [edge_feature.npy](DGB_UNtrade/processed_data/edge_feature.npy) 1.9MB, 507,498×1, int32 | yearly<br>range=[0->31]<br>unique=32 |
+| [UNvote](DGB_UNvote/processed_data/edges.csv) (num_edges: 1,035,742) | range: 1->201<br>unique: 201 | range: 1->201<br>unique: 201 | UN General Assembly roll-call votes.<br>Edge weight increases when two nations both vote "yes". | [edge_feature.npy](DGB_UNvote/processed_data/edge_feature.npy) 2.0MB, 1,035,743×1, int16 | yearly<br>range=[0->71]<br>unique=72 |
+| [USLegis](DGB_USLegis/processed_data/edges.csv) (num_edges: 60,396) | range: 1->225<br>unique: 224 | range: 1->225<br>unique: 225 | US Senate co-sponsorship network.<br>Edge weight = number of shared bill co-sponsorships in a congress. | [edge_feature.npy](DGB_USLegis/processed_data/edge_feature.npy) 118.1KB, 60,397×1, int16 | bi-yearly<br>range=[0->11]<br>unique=12 |
+| [Enron](DGB_enron/processed_data/edges.csv) (num_edges: 125,235) | range: 1->184<br>unique: 181 | range: 1->184<br>unique: 184 | Email communications between Enron employees over three years. | n/a | Second<br>range=[0->113,740,399]<br>unique=22,632 |
+| [LastFM](DGB_lastfm/processed_data/edges.csv) (num_edges: 1,293,103) | range: 1->980<br>unique: 980 | range: 981->1980<br>unique: 1000 | Bipartite user-song listening graph over one month. | [node_role.npy](DGB_lastfm/processed_data/node_role.npy) 2.1KB, 1,981×1, bool | Second<br>range=[0->137,107,267]<br>unique=1,283,614 |
+| [MOOC](DGB_mooc/processed_data/edges.csv) (num_edges: 411,749) | range: 1->7047<br>unique: 7047 | range: 7048->7144<br>unique: 97 | Bipartite student-content interaction graph.<br>Each edge has a 4-dim feature. | [edge_feature.npy](DGB_mooc/processed_data/edge_feature.npy) 12.6MB, 411,750×4, float64<br>[dynamic_node_labels.csv](DGB_mooc/processed_data/dynamic_node_labels.csv) 2.0MB, 411,749×1, bool<br>[node_role.npy](DGB_mooc/processed_data/node_role.npy) 7.1KB, 7,145×1, bool | Second<br>range=[0->2,572,086]<br>unique=345,600 |
+| [Reddit](DGB_reddit/processed_data/edges.csv) (num_edges: 672,447) | range: 1->10000<br>unique: 10000 | range: 10001->10984<br>unique: 984 | Bipartite user-subreddit posting graph over one month.<br>172-dim LIWC edge feature.<br>Dynamic ban labels. | [edge_feature.npy](DGB_reddit/processed_data/edge_feature.npy) 882.4MB, 672,448×172, float64<br>[dynamic_node_labels.csv](DGB_reddit/processed_data/dynamic_node_labels.csv) 3.3MB, 672,447×1, bool<br>[node_role.npy](DGB_reddit/processed_data/node_role.npy) 10.9KB, 10,985×1, bool | Second<br>range=[0->2,678,390.016]<br>unique=669,065 |
+| [UCI](DGB_uci/processed_data/edges.csv) (num_edges: 59,835) | range: 1->1899<br>unique: 1350 | range: 1->1898<br>unique: 1862 | Online communication network where nodes are university students.<br>Edges are posted messages. | n/a | Second<br>range=[0->16,736,181]<br>unique=58,911 |
+| [Wikipedia](DGB_wikipedia/processed_data/edges.csv) (num_edges: 157,474) | range: 1->8227<br>unique: 8227 | range: 8228->9227<br>unique: 1000 | Bipartite user-page editing graph over one month.<br>172-dim LIWC edge feature.<br>Dynamic temporary-ban labels. | [edge_feature.npy](DGB_wikipedia/processed_data/edge_feature.npy) 206.6MB, 157,475×172, float64<br>[dynamic_node_labels.csv](DGB_wikipedia/processed_data/dynamic_node_labels.csv) 772KB, 157,474×1, bool<br>[node_role.npy](DGB_wikipedia/processed_data/node_role.npy) 9.1KB, 9,228×1, bool | Second<br>range=[0->2,678,373]<br>unique=152,757 |

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TG_network_datasets/raw_data/datasets_readme.md

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+# Towards Better Evaluation for Dynamic Link Prediction
+
+## Dataset Description
+
+For preparing the datasets, we closely follow the baseline methods data preparation strategy.
+The original networks are saved as <network>.csv.
+
+The networks are formatted as follows:
+	* Each edge is denoted in one line.
+	* Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.
+		* Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.
+	* The first line denotes the network format.
+	* Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges.
+
+The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).
+Ater preprocessing the network edge-list, there are three files that are used by the models:
+	* <ml_network>.csv: this file contains the timestamp edge-list.
+	* <ml_network>.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.
+	* <ml_network_node>.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.
+Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.

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