April

README.md

@@ -6,7 +6,11 @@ license: cc-by-4.0
 
 <!-- Provide a quick summary of the dataset. -->
 
-CrediBench is a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview). 
+CrediBench 1.1 is a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview). 
+A prior version of the paper is [available here](https://arxiv.org/abs/2509.23340) (NPGML workshop @ NeurIPS 2025), with the latest version still under review. 
+CrediBench 1.0, presented in this prior work, constituted of a static webgraph with 1 month's data, while the current version contains 3 months of data (October to December 2024, surrounding the U.S Federal elections, a period of increased misinformation). 
+We are actively constructing and uploading more monthly graphs as well.  
+
 
 ## Dataset Details
 
@@ -18,22 +22,25 @@ In these webgraphs, the nodes represent a website domain (e.g, `google.com`) and
 These webgraphs are supplemented with text attributes, partly from Common Crawl and from web scraping, as text features play an important role in misinformation detection. 
 Additionally, we supplement them with credibility scores as made available by [Lin et al.](https://github.com/hauselin/domain-quality-ratings/tree/main/data), to enable supervised and semi-supervised learning as explained in our paper. 
 
+- **Curated by** a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA.
+- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
+*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
+Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
+- **License:** CC-BY-4.0 (as retributed from Common Crawl).
 
 Dataset Statistics: 
 | Month | V | E | Min. deg. | Mean deg. | Max. deg. | Leaves (deg. = 1) | Edge Density | 
 | -- | -- | -- | -- | -- | -- | -- | -- |
 | October 2024 | 50,288,479 | 1,074,971,387 | 1 | 42.75 | 17,112,352 | 30,278 | 4.3e-07 | 
-| November 2024 | 50,684,724 | 1,164,563,814 | 1 | 45.95 | 17,328,063 | 31,388 | 4.5e-07 | 
+| November 2024 (to redo) | 27,567,417 | 555,905,375 | 1 | 40.33 | 9,019,038 | 30,553 | 7.3e-07 | 
 | December 2024 | 45,030,252 | 1,014,523,551 | 1 | 45.06 | 14,719,077 | 28,857 | 5.0e-07 | 
 | January 2025 | 45,626,949 | 1,060,163,646 | 1 | 46.471 | 15,398,279 | 23,130 | 5.0e-07 | 
 | February 2025 | 49,639,664 | 1,167,748,533 | 1 | 47.05 | 17,078,954 | 24,430 | 4.7e-07 | 
 | March 2025 | 50,162,733 | 1,212,826,396 | 1 | 48.36 | 16,691,193 | 22,629 | 4.8e-07 | 
-| April 2025 | 50,050,221 | 1,237,519,870 | 1 | 49.45 | 16,679,192 | 26,639 | 4.9e-07 |
-| May 2025 | 50,517,253 | 1,227,682,479  | 1 | 48.604 | 16,771,274 | 29,973 | 4.8e-07 | 
+| April 2025 (to redo) | 17,998,846 | 349,717,108 | 1 | 38.86 | 5,284,367 | 25,606 | 1.1e-06 |
+<!-- | May 2025 | 5,833,993 | 87,752,862 | 1 | 30.08 | 1,581,282 | 17,683 | 2.6e-06 |  -->
 <!-- | June 2025 | 9,974,275 | 152,449,542 | 1 | 30.57 | 3,381,364 | 25,447 | 1.5e06 |  -->
 
-
-
 **Content Embedding:**
 
 Domain-level content embeddings are generated using multiple LLM-based embedding models with varying LLM-model sizes and embedding dimensions. 
@@ -56,23 +63,6 @@ Each pickled file stores a dictionary:
 | November 2024 | embeddinggemma-300m | 256 | 30GB|
 | December 2024 | embeddinggemma-300m | 256 | 30GB|
 
-
-**Proportion of content**
-
-
-| Task | Split | Class | Global Labels | CDB-Oct24 Labels | CDB-Oct24 Has_Content | CDB-Oct24 content(%) | CDB-Nov24 Labels | CDB-Nov24 Has_Content | CDB-Nov24 content(%) | CDB-Dec24 Labels | CDB-Dec24 Has_Content | CDB-Dec24 content(%) |
-|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| Regression | Train | — | 5,164 | 5,158 | 2,699 | 0.52 | 5,158 | 2,626 | 0.51 | 5,158 | 2,637 | 0.50 |
-| Regression | Val | — | 1,722 | 1,719 | 869 | 0.51 | 1,719 | 842 | 0.49 | 1,719 | 841 | 0.49 |
-| Regression | Test | — | 1,722 | 1,721 | 874 | 0.51 | 1,721 | 847 | 0.49 | 1,721 | 843 | 0.49 |
-| Binary Classification | Train | Credible | 41,671 | 39,824 | 23,170 | 0.58 | 34,040 | 20,379 | 0.60 | 39,445 | 21,853 | 0.55 |
-| Binary Classification | Train | Non-Cred. | 9,353 | 8,272 | 5,734 | 0.69 | 5,496 | 4,034 | 0.73 | 7,831 | 5,040 | 0.64 |
-| Binary Classification | Val | Credible | 3,117 | 2,960 | 1,669 | 0.56 | 2,503 | 1,446 | 0.58 | 2,940 | 1,566 | 0.53 |
-| Binary Classification | Val | Non-Cred. | 3,117 | 2,717 | 1,929 | 0.71 | 1,784 | 1,321 | 0.74 | 2,619 | 1,694 | 0.65 |
-| Binary Classification | Test | Credible | 3,117 | 2,967 | 1,739 | 0.59 | 2,555 | 1,543 | 0.60 | 2,947 | 1,629 | 0.55 |
-| Binary Classification | Test | Non-Cred. | 3,117 | 2,713 | 1,920 | 0.71 | 1,782 | 1,328 | 0.75 | 2,620 | 1,704 | 0.65 |
-
-
 ### Resources
 
 <!-- Provide the basic links for the dataset. -->
@@ -106,15 +96,6 @@ which builds a month's worth of data by pulling from Common Crawl, builds the gr
 Each edge has a timestamp, given as the date of the first day of week of the crawl, in format YYYYMMDD.
 
 
-## Acknowledgements 
-
-- **Curated by** a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA.
-- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
-*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
-Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
-- **License:** CC-BY-4.0 (as retributed from Common Crawl).
-
-
 ## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

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