3 research outputs found
Feature Interaction-aware Graph Neural Networks
Inspired by the immense success of deep learning, graph neural networks
(GNNs) are widely used to learn powerful node representations and have
demonstrated promising performance on different graph learning tasks. However,
most real-world graphs often come with high-dimensional and sparse node
features, rendering the learned node representations from existing GNN
architectures less expressive. In this paper, we propose \textit{Feature
Interaction-aware Graph Neural Networks (FI-GNNs)}, a plug-and-play GNN
framework for learning node representations encoded with informative feature
interactions. Specifically, the proposed framework is able to highlight
informative feature interactions in a personalized manner and further learn
highly expressive node representations on feature-sparse graphs. Extensive
experiments on various datasets demonstrate the superior capability of FI-GNNs
for graph learning tasks
Graph Factorization Machines for Cross-Domain Recommendation
Recently, graph neural networks (GNNs) have been successfully applied to
recommender systems. In recommender systems, the user's feedback behavior on an
item is usually the result of multiple factors acting at the same time.
However, a long-standing challenge is how to effectively aggregate multi-order
interactions in GNN. In this paper, we propose a Graph Factorization Machine
(GFM) which utilizes the popular Factorization Machine to aggregate multi-order
interactions from neighborhood for recommendation. Meanwhile, cross-domain
recommendation has emerged as a viable method to solve the data sparsity
problem in recommender systems. However, most existing cross-domain
recommendation methods might fail when confronting the graph-structured data.
In order to tackle the problem, we propose a general cross-domain
recommendation framework which can be applied not only to the proposed GFM, but
also to other GNN models. We conduct experiments on four pairs of datasets to
demonstrate the superior performance of the GFM. Besides, based on general
cross-domain recommendation experiments, we also demonstrate that our
cross-domain framework could not only contribute to the cross-domain
recommendation task with the GFM, but also be universal and expandable for
various existing GNN models
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated
content on social media is a growing concern, especially with the ease of
access to such sources, and the lack of awareness of the existence of such
false information. In this paper, we present an overview of the techniques
explored to date for the combating of disinformation with various forms. We
introduce different forms of disinformation, discuss factors related to the
spread of disinformation, elaborate on the inherent challenges in detecting
disinformation, and show some approaches to mitigating disinformation via
education, research, and collaboration. Looking ahead, we present some
promising future research directions on disinformation.Comment: WIREs Data Mining and Knowledge Discover