GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
This work addresses the need for standardized tools and benchmarks in graph source localization research, though it is incremental as it builds on existing approaches without introducing new methods.
The authors tackled the problem of graph source localization by introducing GraphSL, an open-source library that provides tools for simulating information diffusions and evaluating state-of-the-art source localization methods on benchmark datasets, resulting in a publicly available resource for researchers.
We introduce GraphSL, a new library for studying the graph source localization problem. graph diffusion and graph source localization are inverse problems in nature: graph diffusion predicts information diffusions from information sources, while graph source localization predicts information sources from information diffusions. GraphSL facilitates the exploration of various graph diffusion models for simulating information diffusions and enables the evaluation of cutting-edge source localization approaches on established benchmark datasets. The source code of GraphSL is made available at Github Repository (https://github.com/xianggebenben/GraphSL). Bug reports and feedback can be directed to the Github issues page (https://github.com/xianggebenben/GraphSL/issues).