174 research outputs found
XFlow: Benchmarking Flow Behaviors over Graphs
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/XFlo
Countering Misinformation on Social Networks Using Graph Alterations
We restrict the propagation of misinformation in a social-media-like
environment while preserving the spread of correct information. We model the
environment as a random network of users in which each news item propagates in
the network in consecutive cascades. Existing studies suggest that the cascade
behaviors of misinformation and correct information are affected differently by
user polarization and reflexivity. We show that this difference can be used to
alter network dynamics in a way that selectively hinders the spread of
misinformation content. To implement these alterations, we introduce an
optimization-based probabilistic dropout method that randomly removes
connections between users to achieve minimal propagation of misinformation. We
use disciplined convex programming to optimize these removal probabilities over
a reduced space of possible network alterations. We test the algorithm's
effectiveness using simulated social networks. In our tests, we use both
synthetic network structures based on stochastic block models, and natural
network structures that are generated using random sampling of a dataset
collected from Twitter. The results show that on average the algorithm
decreases the cascade size of misinformation content by up to in
synthetic network tests and up to in natural network tests while
maintaining a branching ratio of at least for correct information.Comment: 10 pages, 6 figure
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
A new era of Information Warfare has arrived. Various actors, including
state-sponsored ones, are weaponizing information on Online Social Networks to
run false information campaigns with targeted manipulation of public opinion on
specific topics. These false information campaigns can have dire consequences
to the public: mutating their opinions and actions, especially with respect to
critical world events like major elections. Evidently, the problem of false
information on the Web is a crucial one, and needs increased public awareness,
as well as immediate attention from law enforcement agencies, public
institutions, and in particular, the research community. In this paper, we make
a step in this direction by providing a typology of the Web's false information
ecosystem, comprising various types of false information, actors, and their
motives. We report a comprehensive overview of existing research on the false
information ecosystem by identifying several lines of work: 1) how the public
perceives false information; 2) understanding the propagation of false
information; 3) detecting and containing false information on the Web; and 4)
false information on the political stage. In this work, we pay particular
attention to political false information as: 1) it can have dire consequences
to the community (e.g., when election results are mutated) and 2) previous work
show that this type of false information propagates faster and further when
compared to other types of false information. Finally, for each of these lines
of work, we report several future research directions that can help us better
understand and mitigate the emerging problem of false information dissemination
on the Web
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