XFlow: Benchmarking Flow Behaviors over Graphs
Zijian Zhang Zonghan Zhang Zhiqian Chen
The occurrence of diffusion on a graph is a prevalent and significant
phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart
grid failures, and similar events. Comprehending the behaviors of flow is a
formidable task, due to the intricate interplay between the distribution of
seeds that initiate flow propagation, the propagation model, and the topology
of the graph. The study of networks encompasses a diverse range of academic
disciplines, including mathematics, physics, social science, and computer
science. This interdisciplinary nature of network research is characterized by
a high degree of specialization and compartmentalization, and the cooperation
facilitated by them is inadequate. From a machine learning standpoint, there is
a deficiency in a cohesive platform for assessing algorithms across various
domains. One of the primary obstacles to current research in this field is the
absence of a comprehensive curated benchmark suite to study the flow behaviors
under network scenarios.
  To address this disparity, we propose the implementation of a novel benchmark
suite that encompasses a variety of tasks, baseline models, graph datasets, and
evaluation tools. In addition, we present a comprehensive analytical framework
that offers a generalized approach to numerous flow-related tasks across
diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes
of our empirical investigation, we analyze the advantages and disadvantages of
current foundational models, and we underscore potential avenues for further
study. The datasets, code, and baseline models have been made available for the
public at: https://github.com/XGraphing/XFlow