2,579 research outputs found
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Graph clustering, which learns the node representations for effective cluster
assignments, is a fundamental yet challenging task in data analysis and has
received considerable attention accompanied by graph neural networks in recent
years. However, most existing methods overlook the inherent relational
information among the non-independent and non-identically distributed nodes in
a graph. Due to the lack of exploration of relational attributes, the semantic
information of the graph-structured data fails to be fully exploited which
leads to poor clustering performance. In this paper, we propose a novel
self-supervised deep graph clustering method named Relational Redundancy-Free
Graph Clustering (RFGC) to tackle the problem. It extracts the attribute-
and structure-level relational information from both global and local views
based on an autoencoder and a graph autoencoder. To obtain effective
representations of the semantic information, we preserve the consistent
relation among augmented nodes, whereas the redundant relation is further
reduced for learning discriminative embeddings. In addition, a simple yet valid
strategy is utilized to alleviate the over-smoothing issue. Extensive
experiments are performed on widely used benchmark datasets to validate the
superiority of our RFGC over state-of-the-art baselines. Our codes are
available at https://github.com/yisiyu95/R2FGC.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems
(TNNLS 2024
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
Rethinking and Simplifying Bootstrapped Graph Latents
Graph contrastive learning (GCL) has emerged as a representative paradigm in
graph self-supervised learning, where negative samples are commonly regarded as
the key to preventing model collapse and producing distinguishable
representations. Recent studies have shown that GCL without negative samples
can achieve state-of-the-art performance as well as scalability improvement,
with bootstrapped graph latent (BGRL) as a prominent step forward. However,
BGRL relies on a complex architecture to maintain the ability to scatter
representations, and the underlying mechanisms enabling the success remain
largely unexplored. In this paper, we introduce an instance-level decorrelation
perspective to tackle the aforementioned issue and leverage it as a springboard
to reveal the potential unnecessary model complexity within BGRL. Based on our
findings, we present SGCL, a simple yet effective GCL framework that utilizes
the outputs from two consecutive iterations as positive pairs, eliminating the
negative samples. SGCL only requires a single graph augmentation and a single
graph encoder without additional parameters. Extensive experiments conducted on
various graph benchmarks demonstrate that SGCL can achieve competitive
performance with fewer parameters, lower time and space costs, and significant
convergence speedup.Comment: Accepted by WSDM 202
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
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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