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GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets

Junxiang Wang, Liang Zhao

TL;DR

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.

Abstract

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).

GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets

TL;DR

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.

Abstract

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).
Paper Structure (4 sections, 2 figures, 1 table)

This paper contains 4 sections, 2 figures, 1 table.

Table of Contents

  1. Summary
  2. Statement of Need
  3. Methods and Benchmark Datasets
  4. Availability and Documentation

Figures (2)

  • Figure 1: An example of graph source localization
  • Figure 2: The hierarchical structure of the GraphSL library: in total six algorithms are implemented, which can be divided into two categories: prescribed methods that rely on hand-crafted rules and GNN-based methods which learn rules from graph data.