5,297 research outputs found
\{kappa}HGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
The prevalence of tree-like structures, encompassing hierarchical structures
and power law distributions, exists extensively in real-world applications,
including recommendation systems, ecosystems, financial networks, social
networks, etc. Recently, the exploitation of hyperbolic space for tree-likeness
modeling has garnered considerable attention owing to its exponential growth
volume. Compared to the flat Euclidean space, the curved hyperbolic space
provides a more amenable and embeddable room, especially for datasets
exhibiting implicit tree-like architectures. However, the intricate nature of
real-world tree-like data presents a considerable challenge, as it frequently
displays a heterogeneous composition of tree-like, flat, and circular regions.
The direct embedding of such heterogeneous structures into a homogeneous
embedding space (i.e., hyperbolic space) inevitably leads to heavy distortions.
To mitigate the aforementioned shortage, this study endeavors to explore the
curvature between discrete structure and continuous learning space, aiming at
encoding the message conveyed by the network topology in the learning process,
thereby improving tree-likeness modeling. To the end, a curvature-aware
hyperbolic graph convolutional neural network, \{kappa}HGCN, is proposed, which
utilizes the curvature to guide message passing and improve long-range
propagation. Extensive experiments on node classification and link prediction
tasks verify the superiority of the proposal as it consistently outperforms
various competitive models by a large margin.Comment: KDD 202
Neural Ideal Point Estimation Network
Understanding politics is challenging because the politics take the influence
from everything. Even we limit ourselves to the political context in the
legislative processes; we need a better understanding of latent factors, such
as legislators, bills, their ideal points, and their relations. From the
modeling perspective, this is difficult 1) because these observations lie in a
high dimension that requires learning on low dimensional representations, and
2) because these observations require complex probabilistic modeling with
latent variables to reflect the causalities. This paper presents a new model to
reflect and understand this political setting, NIPEN, including factors
mentioned above in the legislation. We propose two versions of NIPEN: one is a
hybrid model of deep learning and probabilistic graphical model, and the other
model is a neural tensor model. Our result indicates that NIPEN successfully
learns the manifold of the legislative bill texts, and NIPEN utilizes the
learned low-dimensional latent variables to increase the prediction performance
of legislators' votings. Additionally, by virtue of being a domain-rich
probabilistic model, NIPEN shows the hidden strength of the legislators' trust
network and their various characteristics on casting votes
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