Threadle: A Memory-Efficient Network Storage and Query Engine for Large, Multilayer, and Mixed-mode Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866915835015069696 |
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| author | Nordlund, Carl Jiao, Yukun |
| author_facet | Nordlund, Carl Jiao, Yukun |
| contents | We present Threadle, an open-source, high-performance, and memory-efficient network storage and query engine written in C#. Designed for working with full-population networks derived from administrative register data, which represent very large, multilayer, mixed-mode networks with millions of nodes and billions of edges, Threadle addresses a fundamental limitation of existing network libraries: the inability to efficiently handle two-mode (bipartite) data at scale. Threadle's core innovation is a pseudo-projection approach that allows two-mode layers to be queried as if they were projected into one-mode form, without ever materializing the memory-prohibitive projection. We demonstrate that a network with 20 million nodes containing layers equivalent to 8 trillion projected edges can be stored in approximately 20 GB of RAM -- a compression ratio exceeding 2000:1 compared to materialized projection. Additionally, Threadle provides native support for multilayer mixed-mode networks, an integrated node attribute manager, and a CLI frontend with 50+ commands for the construction, processing, file handling, and management of very large heterogeneous networks. Threadle is freely available at https://www.threadle.dev and can either be obtained as precompiled binaries for Win, macOS and Linux, or compiled directly from source. Supplementing Threadle is threadleR, an R frontend that enables advanced sampling- and traversal-based analyses on very large, heterogeneous, multilayer, mixed-mode population-scale networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_04446 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Threadle: A Memory-Efficient Network Storage and Query Engine for Large, Multilayer, and Mixed-mode Networks Nordlund, Carl Jiao, Yukun Networking and Internet Architecture Mathematical Software Social and Information Networks We present Threadle, an open-source, high-performance, and memory-efficient network storage and query engine written in C#. Designed for working with full-population networks derived from administrative register data, which represent very large, multilayer, mixed-mode networks with millions of nodes and billions of edges, Threadle addresses a fundamental limitation of existing network libraries: the inability to efficiently handle two-mode (bipartite) data at scale. Threadle's core innovation is a pseudo-projection approach that allows two-mode layers to be queried as if they were projected into one-mode form, without ever materializing the memory-prohibitive projection. We demonstrate that a network with 20 million nodes containing layers equivalent to 8 trillion projected edges can be stored in approximately 20 GB of RAM -- a compression ratio exceeding 2000:1 compared to materialized projection. Additionally, Threadle provides native support for multilayer mixed-mode networks, an integrated node attribute manager, and a CLI frontend with 50+ commands for the construction, processing, file handling, and management of very large heterogeneous networks. Threadle is freely available at https://www.threadle.dev and can either be obtained as precompiled binaries for Win, macOS and Linux, or compiled directly from source. Supplementing Threadle is threadleR, an R frontend that enables advanced sampling- and traversal-based analyses on very large, heterogeneous, multilayer, mixed-mode population-scale networks. |
| title | Threadle: A Memory-Efficient Network Storage and Query Engine for Large, Multilayer, and Mixed-mode Networks |
| topic | Networking and Internet Architecture Mathematical Software Social and Information Networks |
| url | https://arxiv.org/abs/2603.04446 |