Star Topology Convolution for Graph Representation Learning

<div><div><div><p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature space. Unlike most existing spectral convolutional methods, this method learns subgraphs which have a star topology rather than a fixed graph. It has fewer parameters in its convolutional filter and is inductive so that it is more flexible and can be applied to large and evolving graphs. As for CNNs in Euclidean feature space, the convolutional filter is localized and maintains a good weight sharing property. By introducing deep layers, the method can learn global features like a CNN. To validate the method, STC was compared to state-of-the-art spectral convolutional and spatial convolutional methods in a supervised learning setting on three benchmark datasets: Cora, Citeseer and Pubmed. The experimental results show that STC outperforms the other methods. STC was also applied to protein identification tasks and outperformed traditional and advanced protein identification methods.</p></div></div></div>

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Star Topology Convolution for Graph Representation Learning

10.36227/techrxiv.12805799 ◽

2020 ◽

Author(s):

Chong Wu ◽

Zhenan Feng ◽

Jiangbin Zheng ◽

Houwang Zhang ◽

Jiawang Cao ◽

...

Keyword(s):

Protein Identification ◽

State Of The Art ◽

Feature Space ◽

Representation Learning ◽

Graph Representation ◽

Global Features ◽

Star Topology ◽

Identification Methods ◽

Benchmark Datasets ◽

Deep Layers

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Star Topology Convolution for Graph Representation Learning

10.36227/techrxiv.12805799.v1 ◽

2020 ◽

Author(s):

Chong Wu ◽

Zhenan Feng ◽

Jiangbin Zheng ◽

Houwang Zhang ◽

Jiawang Cao ◽

...

Keyword(s):

Protein Identification ◽

State Of The Art ◽

Feature Space ◽

Representation Learning ◽

Graph Representation ◽

Convolution Kernel ◽

Star Topology ◽

Identification Methods ◽

Feature Spaces ◽

Benchmark Datasets

<div><div><div><p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional in neural networks (CNNs) in Euclidean feature space. Unlike most existing spectral convolution methods, this method learns subgraphs which have a star topology rather than a fixed graph. It has fewer parameters in its convolution kernel and is inductive so that it is more flexible and can be applied to large and evolving graphs. As for CNNs in Euclidean feature spaces, the convolution kernel is localized and maintains good sharing. By increasing the depth of a layer, the method can learn lobal features like a CNN. To validate the method, STC was compared to state-of-the-art spectral convolution and spatial convolution methods in a supervised learning setting on three benchmark datasets: Cora, Citeseer and Pubmed. The experimental results show that STC outperforms the other methods. STC was also applied to protein identification tasks and outperformed traditional and advanced protein identification methods.</p></div></div></div>

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Star Topology Convolution for Graph Representation Learning

10.36227/techrxiv.12805799.v3 ◽

2021 ◽

Author(s):

Chong Wu ◽

Zhenan Feng ◽

Jiangbin Zheng ◽

Houwang Zhang ◽

Jiawang Cao ◽

...

Keyword(s):

Spectral Methods ◽

Protein Identification ◽

State Of The Art ◽

Feature Space ◽

Representation Learning ◽

Graph Representation ◽

Large Graphs ◽

Star Topology ◽

Essential Protein ◽

Benchmark Datasets

<p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature space. Unlike most existing spectral methods, this method learns subgraphs which have a star topology rather than a fixed graph. Due to the good properties of a star topology, STC is graph/subgraph free. It has fewer parameters in its convolutional filter and is inductive so that it is more flexible and can be applied to large and evolving graphs. Similar to CNNs in Euclidean feature space, the convolutional filter is learnable and localized and maintains a good weight sharing property. To test the method, STC was compared to state-of-the-art spectral methods and spatial methods in a supervised learning setting on five benchmark datasets: Cora, Citeseer, Pubmed, Ogbn-Arxiv, and Ogbn-MAG. The experiment results show that STC outperforms other methods especially on large graphs. In an essential protein identification task, STC also outperforms state-of-the-art essential protein identification methods.<br></p>

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Star Topology Convolution for Graph Representation Learning

10.36227/techrxiv.12805799.v4 ◽

2021 ◽

Author(s):

Chong Wu ◽

Zhenan Feng ◽

Jiangbin Zheng ◽

Houwang Zhang ◽

Jiawang Cao ◽

...

Keyword(s):

Protein Identification ◽

State Of The Art ◽

Representation Learning ◽

Graph Representation ◽

Star Topology ◽

Scale Free ◽

Essential Protein ◽

Feature Spaces ◽

Graph Size ◽

Benchmark Datasets

<p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and maintains a good weight sharing property. To test the method, STC was compared with state-of-the-art graph convolutional methods in a supervised learning setting on six node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Ogbn-Arxiv, and Ogbn-MAG. The experimental results showed that STC achieved state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed state-of-the-art essential protein identification methods.</p>

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Text-Graph Enhanced Knowledge Graph Representation Learning

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.697856 ◽

2021 ◽

Vol 4 ◽

Author(s):

Linmei Hu ◽

Mengmei Zhang ◽

Shaohua Li ◽

Jinghan Shi ◽

Chuan Shi ◽

...

Keyword(s):

State Of The Art ◽

Representation Learning ◽

Graph Representation ◽

Semantic Relationships ◽

Convolutional Networks ◽

Gating Mechanism ◽

Benchmark Datasets ◽

Knowledge Graphs ◽

Low Dimensional ◽

Embedding Methods

Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space. Conventional KG embedding methods (such as TransE and ConvE) utilize only KG triplets and thus suffer from structure sparsity. Some recent works address this issue by incorporating auxiliary texts of entities, typically entity descriptions. However, these methods usually focus only on local consecutive word sequences, but seldom explicitly use global word co-occurrence information in a corpus. In this paper, we propose to model the whole auxiliary text corpus with a graph and present an end-to-end text-graph enhanced KG embedding model, named Teger. Specifically, we model the auxiliary texts with a heterogeneous entity-word graph (called text-graph), which entails both local and global semantic relationships among entities and words. We then apply graph convolutional networks to learn informative entity embeddings that aggregate high-order neighborhood information. These embeddings are further integrated with the KG triplet embeddings via a gating mechanism, thus enriching the KG representations and alleviating the inherent structure sparsity. Experiments on benchmark datasets show that our method significantly outperforms several state-of-the-art methods.

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Efficient Graph Collaborative Filtering via Contrastive Learning

Sensors ◽

10.3390/s21144666 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4666

Author(s):

Zhiqiang Pan ◽

Honghui Chen

Keyword(s):

Collaborative Filtering ◽

State Of The Art ◽

Representation Learning ◽

First Order ◽

Satisfactory Performance ◽

Potential Applications ◽

Benchmark Datasets ◽

Bayesian Personalized Ranking ◽

Graph Neural Networks ◽

Training Efficiency

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

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Hierarchical and Unsupervised Graph Representation Learning with Loukas’s Coarsening

Algorithms ◽

10.3390/a13090206 ◽

2020 ◽

Vol 13 (9) ◽

pp. 206

Author(s):

Louis Béthune ◽

Yacouba Kaloga ◽

Pierre Borgnat ◽

Aurélien Garivier ◽

Amaury Habrard

Keyword(s):

State Of The Art ◽

Back Propagation ◽

Representation Learning ◽

Graph Representation ◽

High Quality ◽

Attributed Graphs ◽

Information Maximization ◽

Classification Tasks ◽

Micro Structures ◽

Mutual Information Maximization

We propose a novel algorithm for unsupervised graph representation learning with attributed graphs. It combines three advantages addressing some current limitations of the literature: (i) The model is inductive: it can embed new graphs without re-training in the presence of new data; (ii) The method takes into account both micro-structures and macro-structures by looking at the attributed graphs at different scales; (iii) The model is end-to-end differentiable: it is a building block that can be plugged into deep learning pipelines and allows for back-propagation. We show that combining a coarsening method having strong theoretical guarantees with mutual information maximization suffices to produce high quality embeddings. We evaluate them on classification tasks with common benchmarks of the literature. We show that our algorithm is competitive with state of the art among unsupervised graph representation learning methods.

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Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

BMC Genomics ◽

10.1186/s12864-020-07033-8 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Zhixun Zhao ◽

Xiaocai Zhang ◽

Fang Chen ◽

Liang Fang ◽

Jinyan Li

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Case Studies ◽

Learning Algorithm ◽

State Of The Art ◽

Learning Algorithms ◽

Feature Space ◽

Sequence Features ◽

Independent Test ◽

Benchmark Datasets

Abstract Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

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Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6821 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11547-11554

Author(s):

Bo Liu ◽

Qiulei Dong ◽

Zhanyi Hu

Keyword(s):

State Of The Art ◽

Feature Space ◽

Visual Features ◽

Selection Strategy ◽

Semantic Features ◽

Visual Feature ◽

Adversarial Network ◽

Benchmark Datasets ◽

Residual Generator ◽

Object Features

Recently, many zero-shot learning (ZSL) methods focused on learning discriminative object features in an embedding feature space, however, the distributions of the unseen-class features learned by these methods are prone to be partly overlapped, resulting in inaccurate object recognition. Addressing this problem, we propose a novel adversarial network to synthesize compact semantic visual features for ZSL, consisting of a residual generator, a prototype predictor, and a discriminator. The residual generator is to generate the visual feature residual, which is integrated with a visual prototype predicted via the prototype predictor for synthesizing the visual feature. The discriminator is to distinguish the synthetic visual features from the real ones extracted from an existing categorization CNN. Since the generated residuals are generally numerically much smaller than the distances among all the prototypes, the distributions of the unseen-class features synthesized by the proposed network are less overlapped. In addition, considering that the visual features from categorization CNNs are generally inconsistent with their semantic features, a simple feature selection strategy is introduced for extracting more compact semantic visual features. Extensive experimental results on six benchmark datasets demonstrate that our method could achieve a significantly better performance than existing state-of-the-art methods by ∼1.2-13.2% in most cases.

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Kernel k-MACE: hypercube unsupervised clustering method

10.32920/ryerson.14661465 ◽

2021 ◽

Author(s):

Faizan Ur Rahman ◽

Soosan Beheshti

Keyword(s):

Kernel Function ◽

State Of The Art ◽

Feature Space ◽

Unsupervised Clustering ◽

Gaussian Kernel ◽

Clustering Methods ◽

Number Of Clusters ◽

Kernel Parameter ◽

Benchmark Datasets ◽

Multiple State

Transforming data to feature space using a kernel function can result in better expression of its features, resulting in better separability for some datasets. The parameters of the kernel function govern the structure of data in feature space and need to be optimized simultaneously while also estimating the number of clusters in a dataset. The proposed method denoted by kernel k-Minimum Average Central Error (kernel k-MACE), esti- mates the number of clusters in a dataset while simultaneously clustering the dataset in feature space by finding the optimum value of the Gaussian kernel parameter σk. A cluster initialization technique has also been proposed based on an existing method for k-means clustering. Simulations show that for self-generated datasets with Gaus- sian clusters having 10% - 50% overlap and for real benchmark datasets, the proposed method outperforms multiple state-of-the-art unsupervised clustering methods including k-MACE, the clustering scheme that inspired kernel k-MACE.

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