About the Journal
Aims and Scope. Journal of Graph Algorithms and Network Optimization provides a focused venue for research on shortest paths, flows, cuts, matching, graph partitioning. The journal prioritizes technically substantive, internationally relevant work in which the principal novelty lies inside the stated computing scope rather than only in the application domain.
Core topics in scope include:
- shortest paths
- flows
- cuts
- matching
- graph partitioning
- dynamic graphs
- large-scale graph optimization
- graph algorithms
- combinatorial optimization
- network optimization
Evidence and methodological expectations. Algorithmic claims should be supported by correctness arguments, complexity analysis or provable properties where applicable, and empirical benchmarks against competitive baselines on representative instances. New heuristics must include sensitivity, scalability, and failure-case analysis.
Normally outside scope. Routine application of standard algorithms, parameter tuning without algorithmic insight, and optimization studies lacking complexity, convergence, approximation, or rigorous benchmark evidence are normally outside scope.
Research integrity and reproducibility. Authors should disclose datasets, software, model or system configurations, experimental protocols, statistical procedures, ethical approvals where applicable, competing interests, funding, and any material use of generative AI. Data and code should be shared when legally and ethically possible, or the restriction must be explained.
