190 research outputs found
Equity evaluation of urban park system: a case study of Xiamen, China
Urban parks play a distinctive and important role in satisfying residents’ demands on leisure and recreation, and thus have become the focus of research in the field of urban planning and sustainable development. This paper used equity as indicator to combine both the supply and demand sides of urban park service. Taking Xiamen as the study case, the relationship between spatial distribution of population and park services was analyzed. The results show that while population density has a significant spatial relationship with urban park service level at the city scale, Xiamen has the problem of neglecting the equity of urban park service between people and regions within the city. The proposed approach builds up the linkage between urban park service and urban population in order to evaluate the performance of urban park. Although the mechanism remains to be discussed, this study provides a useful auxiliary tool for constructing a guideline for urban green space planning, since urban park is increasingly seen as a kind of restricted public resource and ensuring its equity should be an important task for city mangers
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is
becoming a hot research spot, which aims to mine the potential relationships
between different views. Most existing DCMVC algorithms focus on exploring the
consistency information for the deep semantic features, while ignoring the
diverse information on shallow features. To fill this gap, we propose a novel
multi-view clustering network termed CodingNet to explore the diverse and
consistent information simultaneously in this paper. Specifically, instead of
utilizing the conventional auto-encoder, we design an asymmetric structure
network to extract shallow and deep features separately. Then, by aligning the
similarity matrix on the shallow feature to the zero matrix, we ensure the
diversity for the shallow features, thus offering a better description of
multi-view data. Moreover, we propose a dual contrastive mechanism that
maintains consistency for deep features at both view-feature and pseudo-label
levels. Our framework's efficacy is validated through extensive experiments on
six widely used benchmark datasets, outperforming most state-of-the-art
multi-view clustering algorithms
Attribute Graph Clustering via Learnable Augmentation
Contrastive deep graph clustering (CDGC) utilizes contrastive learning to
group nodes into different clusters. Better augmentation techniques benefit the
quality of the contrastive samples, thus being one of key factors to improve
performance. However, the augmentation samples in existing methods are always
predefined by human experiences, and agnostic from the downstream task
clustering, thus leading to high human resource costs and poor performance. To
this end, we propose an Attribute Graph Clustering method via Learnable
Augmentation (\textbf{AGCLA}), which introduces learnable augmentors for
high-quality and suitable augmented samples for CDGC. Specifically, we design
two learnable augmentors for attribute and structure information, respectively.
Besides, two refinement matrices, including the high-confidence pseudo-label
matrix and the cross-view sample similarity matrix, are generated to improve
the reliability of the learned affinity matrix. During the training procedure,
we notice that there exist differences between the optimization goals for
training learnable augmentors and contrastive learning networks. In other
words, we should both guarantee the consistency of the embeddings as well as
the diversity of the augmented samples. Thus, an adversarial learning mechanism
is designed in our method. Moreover, a two-stage training strategy is leveraged
for the high-confidence refinement matrices. Extensive experimental results
demonstrate the effectiveness of AGCLA on six benchmark datasets
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Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis
GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer
Dink-Net: Neural Clustering on Large Graphs
Deep graph clustering, which aims to group the nodes of a graph into disjoint
clusters with deep neural networks, has achieved promising progress in recent
years. However, the existing methods fail to scale to the large graph with
million nodes. To solve this problem, a scalable deep graph clustering method
(Dink-Net) is proposed with the idea of dilation and shrink. Firstly, by
discriminating nodes, whether being corrupted by augmentations, representations
are learned in a self-supervised manner. Meanwhile, the cluster centres are
initialized as learnable neural parameters. Subsequently, the clustering
distribution is optimized by minimizing the proposed cluster dilation loss and
cluster shrink loss in an adversarial manner. By these settings, we unify the
two-step clustering, i.e., representation learning and clustering optimization,
into an end-to-end framework, guiding the network to learn clustering-friendly
features. Besides, Dink-Net scales well to large graphs since the designed loss
functions adopt the mini-batch data to optimize the clustering distribution
even without performance drops. Both experimental results and theoretical
analyses demonstrate the superiority of our method. Compared to the runner-up,
Dink-Net achieves 9.62% NMI improvement on the ogbn-papers100M dataset with 111
million nodes and 1.6 billion edges. The source code is released at
https://github.com/yueliu1999/Dink-Net. Besides, a collection (papers, codes,
and datasets) of deep graph clustering is shared at
https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.Comment: 19 pages, 5 figure
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Temporal graph learning aims to generate high-quality representations for
graph-based tasks along with dynamic information, which has recently drawn
increasing attention. Unlike the static graph, a temporal graph is usually
organized in the form of node interaction sequences over continuous time
instead of an adjacency matrix. Most temporal graph learning methods model
current interactions by combining historical information over time. However,
such methods merely consider the first-order temporal information while
ignoring the important high-order structural information, leading to
sub-optimal performance. To solve this issue, by extracting both temporal and
structural information to learn more informative node representations, we
propose a self-supervised method termed S2T for temporal graph learning. Note
that the first-order temporal information and the high-order structural
information are combined in different ways by the initial node representations
to calculate two conditional intensities, respectively. Then the alignment loss
is introduced to optimize the node representations to be more informative by
narrowing the gap between the two intensities. Concretely, besides modeling
temporal information using historical neighbor sequences, we further consider
the structural information from both local and global levels. At the local
level, we generate structural intensity by aggregating features from the
high-order neighbor sequences. At the global level, a global representation is
generated based on all nodes to adjust the structural intensity according to
the active statuses on different nodes. Extensive experiments demonstrate that
the proposed method S2T achieves at most 10.13% performance improvement
compared with the state-of-the-art competitors on several datasets
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant
attention owing to its high efficiency and the capability to capture
complementary structural information across multiple views. Intuitively, a
high-quality anchor graph plays an essential role in the success of AMVGC.
However, the existing AMVGC methods only consider single-structure information,
i.e., local or global structure, which provides insufficient information for
the learning task. To be specific, the over-scattered global structure leads to
learned anchors failing to depict the cluster partition well. In contrast, the
local structure with an improper similarity measure results in potentially
inaccurate anchor assignment, ultimately leading to sub-optimal clustering
performance. To tackle the issue, we propose a novel anchor-based multi-view
graph clustering framework termed Efficient Multi-View Graph Clustering with
Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified
framework with a theoretical guarantee is designed to capture local and global
information. Besides, EMVGC-LG jointly optimizes anchor construction and graph
learning to enhance the clustering quality. In addition, EMVGC-LG inherits the
linear complexity of existing AMVGC methods respecting the sample number, which
is time-economical and scales well with the data size. Extensive experiments
demonstrate the effectiveness and efficiency of our proposed method.Comment: arXiv admin note: text overlap with arXiv:2308.1654
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