Bitcoin OTC Synthetic Signed Network (NDS-VU)
A NDS-VU signed network repository dataset of 5,882 nodes and 21,492 signed edges (synthetic Bitcoin OTC variant), collected by NDS-VU (Vrije Universiteit Amsterdam). Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Synthetic version of the Bitcoin OTC web-of-trust network with reversed edge polarities, enabling adversarial and robustness testing of signed network algorithms. Suitable for signed network analysis, algorithm benchmarking, robustness testing.
Cite this dataset at
nds-vu-bitcoin-otc-synthetic.echo.directory
Analytics
The full graph has 5,881 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Computed over the full corpus.
Record
- Catalog key
nds-vu-bitcoin-otc-synthetic- Authors
- NDS-VU
- Year
- 2021
- Source paper
- Signed Network Datasets — Bitcoin OTC Synthetic
- DOI / arXiv
- 10.1145/3366423.3380212
- License
- MIT (NDS-VU repository) (MIT)
- Records
- 5,881
- Nodes
- 5,881
- Edges
- 21,492 (directed)
- Nodes are
- user
- Edges are
- trust
- Attributes
- nodes have no attributes; edges have attributes
- Languages
- en
- Collected
- 2010-2014 (2010s)
- Tasks
- Edge sign prediction (binary;
sign) inferred
From the graph: link prediction, community detection
Provenance
- Originally published at
- https://github.com/NDS-VU/signed-network-datasets link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 261.7 KB across 1 files. Checksums in MANIFEST.json.
Citations
Finding large balanced subgraphs in signed networks
Bruno Ordozgoiti, Antonis Matakos, A. Gionis
Journal Article · The Web Conference · 2020
Cited by
- Stable Community Detection in Signed Social Networks
Renjie Sun, Chen Chen et al. · IEEE Transactions on Knowledge and Data Engineering · 2022 · cited 57× - MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
Yixuan He, Michael Perlmutter et al. · LOG IN · 2022 · cited 53× - PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xi-Tong Zhang et al. · LOG IN · 2022 · cited 37× - LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Xun-Kai Li, Mei-Hao Liao et al. · Proceedings of the VLDB Endowment · 2024 · cited 14× - PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xi-Tong Zhang et al. · arXiv.org · 2022 · cited 12×
Show all 10
- Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
Nikolaos Nakis, Abdulkadir Çelikkanat et al. · International Conference on Artificial Intelligence and Statistics · 2023 · cited 11× - Computing Maximum Structural Balanced Cliques in Signed Graphs
Kai Yao, Lijun Chang et al. · IEEE International Conference on Data Engineering · 2022 · cited 10× - Balance Maximization in Signed Networks via Edge Deletions
Kartik Sharma, Iqra Altaf Gillani et al. · Web Search and Data Mining · 2020 · cited 10× - Neural discovery of balance-aware polarized communities
Francesco Gullo, Domenico Mandaglio et al. · Machine-mediated learning · 2024 · cited 9× - On Cohesively Polarized Communities in Signed Networks
Jason Niu, Ahmet Erdem Sarıyüce · The Web Conference · 2023 · cited 9×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@misc{ndsvu_signed,
title = {Signed Network Datasets},
author = {NDS-VU},
year = {2021},
howpublished = {\url{https://github.com/NDS-VU/signed-network-datasets}}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
NDS-VU (2021). Bitcoin OTC Synthetic Signed Network (NDS-VU) [Data set]. https://doi.org/10.1145/3366423.3380212
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
NDS-VU. Bitcoin OTC Synthetic Signed Network (NDS-VU). 2021, doi.org/10.1145/3366423.3380212.