38 research outputs found
GANN: Graph Alignment Neural Network for Semi-Supervised Learning
Graph neural networks (GNNs) have been widely investigated in the field of
semi-supervised graph machine learning. Most methods fail to exploit adequate
graph information when labeled data is limited, leading to the problem of
oversmoothing. To overcome this issue, we propose the Graph Alignment Neural
Network (GANN), a simple and effective graph neural architecture. A unique
learning algorithm with three alignment rules is proposed to thoroughly explore
hidden information for insufficient labels. Firstly, to better investigate
attribute specifics, we suggest the feature alignment rule to align the inner
product of both the attribute and embedding matrices. Secondly, to properly
utilize the higher-order neighbor information, we propose the cluster center
alignment rule, which involves aligning the inner product of the cluster center
matrix with the unit matrix. Finally, to get reliable prediction results with
few labels, we establish the minimum entropy alignment rule by lining up the
prediction probability matrix with its sharpened result. Extensive studies on
graph benchmark datasets demonstrate that GANN can achieve considerable
benefits in semi-supervised node classification and outperform state-of-the-art
competitors
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
Adaptive Radio Frequency Interference Mitigation for Passive Bistatic Radar Using OFDM Waveform
High frequency passive bistatic radar (HFPBR) is a novel and promising technique in development. DRM broadcast exploiting orthogonal frequency division multiplexing (OFDM) technique supplies a good choice for the illuminator of HFPBR. HFPBR works in crowded short wave band. It faces severe radio frequency interference (RFI) problem. In this paper, a theoretical analysis of the range-domain correlation of RFI in OFDM-based HF radar is presented. A RFI mitigation method in the range domain is introduced. After the direct-path wave rejection, the interference subspace is constructed using the echo signals at the reserved range bins. Then RFI in the effective range bins is mitigated by the subspace projection, using the correlation among different range bins. The introduced algorithm is easy to perform in practice and the RFI mitigation performance is evaluated using the experimental data of DRM-based HFPBR
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Benefiting from the strong view-consistent information mining capacity,
multi-view contrastive clustering has attracted plenty of attention in recent
years. However, we observe the following drawback, which limits the clustering
performance from further improvement. The existing multi-view models mainly
focus on the consistency of the same samples in different views while ignoring
the circumstance of similar but different samples in cross-view scenarios. To
solve this problem, we propose a novel Dual contrastive calibration network for
Multi-View Clustering (DealMVC). Specifically, we first design a fusion
mechanism to obtain a global cross-view feature. Then, a global contrastive
calibration loss is proposed by aligning the view feature similarity graph and
the high-confidence pseudo-label graph. Moreover, to utilize the diversity of
multi-view information, we propose a local contrastive calibration loss to
constrain the consistency of pair-wise view features. The feature structure is
regularized by reliable class information, thus guaranteeing similar samples
have similar features in different views. During the training procedure, the
interacted cross-view feature is jointly optimized at both local and global
levels. In comparison with other state-of-the-art approaches, the comprehensive
experimental results obtained from eight benchmark datasets provide substantial
validation of the effectiveness and superiority of our algorithm. We release
the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Contrastive graph node clustering via learnable data augmentation is a hot
research spot in the field of unsupervised graph learning. The existing methods
learn the sampling distribution of a pre-defined augmentation to generate
data-driven augmentations automatically. Although promising clustering
performance has been achieved, we observe that these strategies still rely on
pre-defined augmentations, the semantics of the augmented graph can easily
drift. The reliability of the augmented view semantics for contrastive learning
can not be guaranteed, thus limiting the model performance. To address these
problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable
AugmenTation (COVERT). Specifically, in our method, the data augmentations are
processed by the proposed reversible perturb-recover network. It distills
reliable semantic information by recovering the perturbed latent embeddings.
Moreover, to further guarantee the reliability of semantics, a novel semantic
loss is presented to constrain the network via quantifying the perturbation and
recovery. Lastly, a label-matching mechanism is designed to guide the model by
clustering information through aligning the semantic labels and the selected
high-confidence clustering pseudo labels. Extensive experimental results on
seven datasets demonstrate the effectiveness of the proposed method. We release
the code and appendix of CONVERT at https://github.com/xihongyang1999/CONVERT
on GitHub
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Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters.
The ecological concept of the r-K life history strategy is widely applied in macro-ecology to characterize functional traits of taxa. However, its adoption in microbial communities is limited, owing to the lack of a measureable, convenient functional trait for classification. In this study, we performed an experiment of stepwise organic amendments in triplicate anaerobic digesters. We found that high resource availability significantly favored microbial r-strategists such as Bacillus spp. Incremental resource availability heightened average rRNA operon copy number of microbial community, resulting in a strong, positive correlation (r>0.74, P<0.008). This study quantifies how resource availability manipulations influence microbial community composition and supports the idea that rRNA operon copy number is an ecologically meaningful trait which reflects resource availability