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Amit A. Nanavati

dblp:417/1393 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-4131-9865ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering › mining software repositories
dataset construction
1.012026
GONets: A First-Look Into GitHub Organisation Networks · SIGIR 2026
Empirical software engineering
mining software repositories
1.012026
GONets: A First-Look Into GitHub Organisation Networks · SIGIR 2026

Methods — techniques the papers use, named apart from their topics

data collection pipeline · 1.0bipartite network analysis · 1.0
YearPublicationVenuePosition
2026 GONets: A First-Look Into GitHub Organisation Networks
abstract
The rapid growth of open-source software has made GitHub a central platform for studying large-scale collaboration for software development. Existing datasets and event logs are typically limited to individual projects or user–repository collaboration at the GitHub level. We introduce GONets, a publicly available dataset that provides a direct bipartite representation of user–repository interactions at the organisational level. GONets comprises over 230K GitHub organisations, 2.52M unique users, 4.37M repositories, and nearly 30M contribution edges, enriched with detailed organisational metadata including organisation size, creation date and follower count. We present an automated data collection pipeline that retrieves organisational and membership information via GitHub's API, aggregates user-repository contribution data, and constructs large-scale bipartite networks suitable for network analysis and modelling. Using this dataset, we conduct the first large-scale characterisation of the GitHub organisational ecosystem, analysing organisational attributes and user-repository contribution patterns across a diverse range of organisations. We release the complete dataset and collection pipeline to support reproducible research and facilitate future studies on topics including code reuse diffusion, collaboration dynamics and the security ecosystem of GitHub organisations. The entire dataset can be accessed at: https://zenodo.org/records/18472549.
Hridoy Sankar Dutta, Biswadeep Khan, Parth Mitesh Shah, Amit A. Nanavati
SIGIR4