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title: Xflow
message: >-
  If you use this software, please cite it using the
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type: software
authors:
  - given-names: Zhiqian
    family-names: Chen
    email: zchen@cse.msstate.edusion
    affiliation: Mississippi State
    orcid: 'https://orcid.org/0000-0003-4112-9647'
identifiers:
  - type: doi
    value: 10.48550/arXiv.2308.03819
url: 'https://xflow.network'
abstract: >-
  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.
keywords:
  - network flow
  - graph flow
  - graph neural networks
  - graph learning
  - network science
  - influence maximization
  - source localization
license: MIT