46 research outputs found
Joint Cuts and Matching of Partitions in One Graph
As two fundamental problems, graph cuts and graph matching have been
investigated over decades, resulting in vast literature in these two topics
respectively. However the way of jointly applying and solving graph cuts and
matching receives few attention. In this paper, we first formalize the problem
of simultaneously cutting a graph into two partitions i.e. graph cuts and
establishing their correspondence i.e. graph matching. Then we develop an
optimization algorithm by updating matching and cutting alternatively, provided
with theoretical analysis. The efficacy of our algorithm is verified on both
synthetic dataset and real-world images containing similar regions or
structures
Mind the Label Shift of Augmentation-based Graph OOD Generalization
Out-of-distribution (OOD) generalization is an important issue for Graph
Neural Networks (GNNs). Recent works employ different graph editions to
generate augmented environments and learn an invariant GNN for generalization.
However, the label shift usually occurs in augmentation since graph structural
edition inevitably alters the graph label. This brings inconsistent predictive
relationships among augmented environments, which is harmful to generalization.
To address this issue, we propose \textbf{LiSA}, which generates
label-invariant augmentations to facilitate graph OOD generalization. Instead
of resorting to graph editions, LiSA exploits \textbf{L}abel-\textbf{i}nvariant
\textbf{S}ubgraphs of the training graphs to construct \textbf{A}ugmented
environments. Specifically, LiSA first designs the variational subgraph
generators to extract locally predictive patterns and construct multiple
label-invariant subgraphs efficiently. Then, the subgraphs produced by
different generators are collected to build different augmented environments.
To promote diversity among augmented environments, LiSA further introduces a
tractable energy-based regularization to enlarge pair-wise distances between
the distributions of environments. In this manner, LiSA generates diverse
augmented environments with a consistent predictive relationship and
facilitates learning an invariant GNN. Extensive experiments on node-level and
graph-level OOD benchmarks show that LiSA achieves impressive generalization
performance with different GNN backbones. Code is available on
\url{https://github.com/Samyu0304/LiSA}.Comment: Accepted to CVPR 2023. arXiv admin note: text overlap with
arXiv:2206.0934
Generative Explanations for Graph Neural Network: Methods and Evaluations
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various
graph-related tasks. However, the black-box nature often limits their
interpretability and trustworthiness. Numerous explainability methods have been
proposed to uncover the decision-making logic of GNNs, by generating underlying
explanatory substructures. In this paper, we conduct a comprehensive review of
the existing explanation methods for GNNs from the perspective of graph
generation. Specifically, we propose a unified optimization objective for
generative explanation methods, comprising two sub-objectives: Attribution and
Information constraints. We further demonstrate their specific manifestations
in various generative model architectures and different explanation scenarios.
With the unified objective of the explanation problem, we reveal the shared
characteristics and distinctions among current methods, laying the foundation
for future methodological advancements. Empirical results demonstrate the
advantages and limitations of different explainability approaches in terms of
explanation performance, efficiency, and generalizability
A Review on Quantum Dot‐Based Color Conversion Layers for Mini/Micro‐LED Displays: Packaging, Light Management, and Pixelation
Mini/microlight-emitting diodes (LEDs) are one of the most promising technologies for next-generation displays to meet the requirements of demanding applications, including augmented reality/virtual reality displays, wearable devices, and microprojectors. To realize full-color displays, the strategy of combining miniaturized blue nitride-based LEDs with color conversion layers is promising due to the high efficiencies of the LEDs and the advantageous manufacturing. Quantum dots (QDs), owing to their high photoluminescence quantum yield, small particle size, and solution processability, have emerged as the color conversion material with the most potential for mini/micro-LEDs. However, the integration of QDs into display technologies poses several challenges. From the material side, the stability of QD materials is still challenging. For the case of packaging QDs in a matrix, the dispersion quality of QDs and the light extraction of the emission need to be improved. From the fabrication side, the lack of high-precision mass manufacturing strategies in QD pixelation hinders the widespread application of QDs. Toward the issues above, this review summarizes the research on QD materials for color conversion display in recent years to systematically draw an overview of the packaging strategies, the light management approaches, and the pixelation methods of QD materials toward mini/micro-LED-based display technologies
Rumor Detection with Diverse Counterfactual Evidence
The growth in social media has exacerbated the threat of fake news to
individuals and communities. This draws increasing attention to developing
efficient and timely rumor detection methods. The prevailing approaches resort
to graph neural networks (GNNs) to exploit the post-propagation patterns of the
rumor-spreading process. However, these methods lack inherent interpretation of
rumor detection due to the black-box nature of GNNs. Moreover, these methods
suffer from less robust results as they employ all the propagation patterns for
rumor detection. In this paper, we address the above issues with the proposed
Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our
intuition is to exploit the diverse counterfactual evidence of an event graph
to serve as multi-view interpretations, which are further aggregated for robust
rumor detection results. Specifically, our method first designs a subgraph
generation strategy to efficiently generate different subgraphs of the event
graph. We constrain the removal of these subgraphs to cause the change in rumor
detection results. Thus, these subgraphs naturally serve as counterfactual
evidence for rumor detection. To achieve multi-view interpretation, we design a
diversity loss inspired by Determinantal Point Processes (DPP) to encourage
diversity among the counterfactual evidence. A GNN-based rumor detection model
further aggregates the diverse counterfactual evidence discovered by the
proposed DCE-RD to achieve interpretable and robust rumor detection results.
Extensive experiments on two real-world datasets show the superior performance
of our method. Our code is available at https://github.com/Vicinity111/DCE-RD
Localized Contrastive Learning on Graphs
Contrastive learning methods based on InfoNCE loss are popular in node
representation learning tasks on graph-structured data. However, its reliance
on data augmentation and its quadratic computational complexity might lead to
inconsistency and inefficiency problems. To mitigate these limitations, in this
paper, we introduce a simple yet effective contrastive model named Localized
Graph Contrastive Learning (Local-GCL in short). Local-GCL consists of two key
designs: 1) We fabricate the positive examples for each node directly using its
first-order neighbors, which frees our method from the reliance on
carefully-designed graph augmentations; 2) To improve the efficiency of
contrastive learning on graphs, we devise a kernelized contrastive loss, which
could be approximately computed in linear time and space complexity with
respect to the graph size. We provide theoretical analysis to justify the
effectiveness and rationality of the proposed methods. Experiments on various
datasets with different scales and properties demonstrate that in spite of its
simplicity, Local-GCL achieves quite competitive performance in self-supervised
node representation learning tasks on graphs with various scales and
properties
Recent Progress in Light‐Scattering Porous Polymers and Their Applications
Conventional inorganic-nanoparticles-based scattering systems have dominated many practical applications for years. In contrast, the rise of porous polymers is perceived as a game-changer due to their low cost, facile preparation, and great abundance. One challenging issue to be tackled is the design and fabrication of porous polymers with light-scattering properties comparable to those of inorganic nanoparticles. Taking inspiration from nature (e.g., from white beetles Cyphochilus), scientists have achieved remarkable progress in the field of light-scattering porous polymers and their related applications in recent years. Therefore, here, an up-to-date review about this emerging field is provided. This overview covers materials for making porous polymer structures, detailed fabrication methods, and applications benefitting from their tailorable light-scattering properties. It is envisioned that more bioinspired light-scattering porous polymers will be made to be potential alternatives of conventional nanoparticles-based scatterers
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data
Generative Adversarial Networks (GANs) typically suffer from overfitting when
limited training data is available. To facilitate GAN training, current methods
propose to use data-specific augmentation techniques. Despite the
effectiveness, it is difficult for these methods to scale to practical
applications. In this work, we present ScoreMix, a novel and scalable data
augmentation approach for various image synthesis tasks. We first produce
augmented samples using the convex combinations of the real samples. Then, we
optimize the augmented samples by minimizing the norms of the data scores,
i.e., the gradients of the log-density functions. This procedure enforces the
augmented samples close to the data manifold. To estimate the scores, we train
a deep estimation network with multi-scale score matching. For different image
synthesis tasks, we train the score estimation network using different data. We
do not require the tuning of the hyperparameters or modifications to the
network architecture. The ScoreMix method effectively increases the diversity
of data and reduces the overfitting problem. Moreover, it can be easily
incorporated into existing GAN models with minor modifications. Experimental
results on numerous tasks demonstrate that GAN models equipped with the
ScoreMix method achieve significant improvements