Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network.
Sign Up to like & getrecommendations! Published in 2022 at "Molecular informatics"
DOI: 10.1002/minf.202100247
Abstract: The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property… read more here.
Keywords: dnn model; graph clustering; model; network ... See more keywords
Vienna Graph Clustering
Sign Up to like & getrecommendations! Published in 2020 at "Methods in molecular biology"
DOI: 10.1007/978-1-4939-9873-9_16
Abstract: This paper serves as a user guide to the Vienna graph clustering framework. We review our general memetic algorithm, VieClus, to tackle the graph clustering problem. A key component of our contribution are natural recombine… read more here.
Keywords: graph; biology; graph clustering; vienna graph ... See more keywords
Auto-weighted Multi-view learning for Semi-Supervised graph clustering
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.07.011
Abstract: Abstract Despite the popularity of graph clustering, existing methods are haunted by two problems. One is the implicit assumption that all attributes are treated equally with the same weights. The other is that they treat… read more here.
Keywords: auto weighted; topology; multi view; graph ... See more keywords
CoHomo: A cluster-attribute correlation aware graph clustering framework
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.002
Abstract: Abstract An attributed graph is a graph where nodes are associated with attributes describing their features. Clustering on an attributed graph is to detect clusters that have not only (1) cohesive structure; but also (2)… read more here.
Keywords: clustering framework; correlation; graph; graph clustering ... See more keywords
On a two-truths phenomenon in spectral graph clustering
Sign Up to like & getrecommendations! Published in 2019 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.1814462116
Abstract: Significance Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is of significant current interest, finding applications throughout the sciences. But as with clustering in general, what a particular methodology identifies as… read more here.
Keywords: spectral embedding; spectral graph; two truths; graph clustering ... See more keywords
Capped $l_1$ -Norm Sparse Representation Method for Graph Clustering
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2912773
Abstract: As one of the most popular clustering techniques, graph clustering has attracted many researchers in the field of machine learning and data mining. Generally speaking, graph clustering partitions the data points into different categories according… read more here.
Keywords: method; inline formula; graph; sparse representation ... See more keywords
Local Expansion and Optimization for Higher-Order Graph Clustering
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2019.2923228
Abstract: Graph clustering aims to identify clusters that feature tighter connections between internal nodes than external nodes. We noted that conventional clustering approaches based on a single vertex or edge cannot meet the requirements of clustering… read more here.
Keywords: order graph; expansion optimization; order; local expansion ... See more keywords
Achieving Graph Clustering Privacy Preservation Based on Structure Entropy in Social IoT
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3092185
Abstract: Decoding the real structure from the Social Internet-of-Things (SIoT) network with a large-scale noise structure plays a fundamental role in data mining. Protecting private information from leakage in the mining process and obtaining accurate mining… read more here.
Keywords: information; based structure; structure; graph clustering ... See more keywords
Dynamic Cluster Formation Game for Attributed Graph Clustering
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2017.2772880
Abstract: Besides the topological structure, there are additional information, i.e., node attributes, on top of the plain graphs. Usually, these systems can be well modeled by attributed graphs, where nodes represent component actors, a set of… read more here.
Keywords: dynamic cluster; cluster; cluster formation; attributed graph ... See more keywords
Contextual Correlation Preserving Multiview Featured Graph Clustering
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DOI: 10.1109/tcyb.2019.2926431
Abstract: Graph clustering, which aims at discovering sets of related vertices in graph-structured data, plays a crucial role in various applications, such as social community detection and biological module discovery. With the huge increase in the… read more here.
Keywords: multiview; contextual correlation; pairwise; correlation ... See more keywords
Embedding Graph Auto-Encoder for Graph Clustering.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3158654
Abstract: Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative ability, several graph auto-encoder (GAE) models, which are… read more here.
Keywords: auto encoder; relaxed means; graph; graph clustering ... See more keywords