JGraphT -- A Java library for graph data structures and algorithms
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Abstract (arXiv)
Mathematical software and graph-theoretical algorithmic packages to efficiently model, analyze and query graphs are crucial in an era where large-scale spatial, societal and economic network data are abundantly available. One such package is JGraphT, a programming library which contains very efficient and generic graph data-structures along with a large collection of state-of-the-art algorithms. The library is written in Java with stability, interoperability and performance in mind. A distinctive feature of this library is the ability to model vertices and edges as arbitrary objects, thereby permitting natural representations of many common networks including transportation, social and biological networks. Besides classic graph algorithms such as shortest-paths and spanning-tree algorithms, the library contains numerous advanced algorithms: graph and subgraph isomorphism; matching and flow problems; approximation algorithms for NP-hard problems such as independent set and TSP; and several more exotic algorithms such as Berge graph detection. Due to its versatility and generic design, JGraphT is currently used in large-scale commercial, non-commercial and academic research projects. In this work we describe in detail the design and underlying structure of the library, and discuss its most important features and algorithms. A computational study is conducted to evaluate the performance of JGraphT versus a number of similar libraries. Experiments on a large number of graphs over a variety of popular algorithms show that JGraphT is highly competitive with other established libraries such as NetworkX or the BGL.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2021Computing Diverse Shortest Paths Efficiently: A Theoretical and Experimental Studycited by 2
- 2022Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matchingcited by 2
- 2024ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabricscited by 2
- 2024On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Studycited by 1
- 2026Secure Decentralized Federated Learning via Gossip and Virtual Votingcited by 0
- 2026Secure Decentralized Federated Learning via Gossip and Virtual Voting9 Jul 2026
- 2024On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study11 Feb 2024
- 2024ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabrics9 Feb 2024
- 2022Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching15 Sep 2022
- 2021Computing Diverse Shortest Paths Efficiently: A Theoretical and Experimental Study10 Dec 2021