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CITATION. cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Xflow
message: >-
If you use this software, please cite it using the
metadata from this file.
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