scholarly journals Hierarchical and Unsupervised Graph Representation Learning with Loukas’s Coarsening

Algorithms ◽  
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.

scholarly journals Graph Debiased Contrastive Learning with Joint Representation Clustering

2021 ◽  
Author(s):  
Han Zhao ◽  
Xu Yang ◽  
Zhenru Wang ◽  
Erkun Yang ◽  
Cheng Deng
Keyword(s):  
State Of The Art ◽  
False Negative ◽  
Poor Performance ◽  
Representation Learning ◽  
Graph Representation ◽  
Learning Framework ◽  
Clustering And Classification ◽  
Class Information ◽  
Classification Tasks ◽  
Joint Representation

By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning. However, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To this end, we propose a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering. Specifically, representations can be optimized by aligning with clustered class information, and simultaneously, the optimized representations can promote clustering, leading to more powerful representations and clustering results. More importantly, we randomly select negative samples from the clusters which are different from the positive sample's cluster. In this way, as the supervisory signals, the clustering results can be utilized to effectively decrease the false-negative samples. Extensive experiments on five datasets demonstrate that our method achieves new state-of-the-art results on graph clustering and classification tasks.

scholarly journals Relation Representation Learning Via Signed Graph Mutual Information Maximization for Trust Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 115
Author(s):  
Yongjun Jing ◽  
Hao Wang ◽  
Kun Shao ◽  
Xing Huo
Keyword(s):  
Mutual Information ◽  
Representation Learning ◽  
Signed Graph ◽  
Trust Prediction ◽  
Trust Networks ◽  
Trust Relation ◽  
Information Maximization ◽  
Real World Datasets ◽  
Negative Links ◽  
Mutual Information Maximization

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

scholarly journals Star Topology Convolution for Graph Representation Learning

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

<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>

scholarly journals UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

2021 ◽  
Author(s):  
Jing Huang ◽  
Jie Yang
Keyword(s):  
Neural Networks ◽  
Message Passing ◽  
State Of The Art ◽  
Representation Learning ◽  
Graph Representation ◽  
Challenging Problem ◽  
Unified Framework ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Research Domains

Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4% to 88.8% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at https://github.com/OneForward/UniGNN.

Knowledge-Guided Disentangled Representation Learning for Recommender Systems

2022 ◽  
Vol 40 (1) ◽  
pp. 1-26
Author(s):  
Shanlei Mu ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Siqing Li ◽  
Ji-Rong Wen
Keyword(s):  
Recommender Systems ◽  
Semantic Information ◽  
Representation Learning ◽  
Interaction Data ◽  
Data Sparsity ◽  
Proposed Model ◽  
Information Maximization ◽  
Alignment Strategy ◽  
Real World Datasets ◽  
Mutual Information Maximization

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

Unsupervised Structural Graph Node Representation Learning

2020 ◽  
Author(s):  
Mikel Joaristi
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Structural Information ◽  
Representation Learning ◽  
Graph Representation ◽  
Learning Methods ◽  
Structural Graph ◽  
Connectivity Information ◽  
Latent Space ◽  
Previous State

Unsupervised Graph Representation Learning methods learn a numerical representation of the nodes in a graph. The generated representations encode meaningful information about the nodes' properties, making them a powerful tool for tasks in many areas of study, such as social sciences, biology or communication networks. These methods are particularly interesting because they facilitate the direct use of standard Machine Learning models on graphs. Graph representation learning methods can be divided into two main categories depending on the information they encode, methods preserving the nodes connectivity information, and methods preserving nodes' structural information. Connectivity-based methods focus on encoding relationships between nodes, with neighboring nodes being closer together in the resulting latent space. On the other hand, structure-based methods generate a latent space where nodes serving a similar structural function in the network are encoded close to each other, independently of them being connected or even close to each other in the graph. While there are a lot of works that focus on preserving nodes' connectivity information, only a few works study the problem of encoding nodes' structure, specially in an unsupervised way. In this dissertation, we demonstrate that properly encoding nodes' structural information is fundamental for many real-world applications, as it can be leveraged to successfully solve many tasks where connectivity-based methods fail. One concrete example is presented first. In this example, the task consists of detecting malicious entities in a real-world financial network. We show that to solve this problem, connectivity information is not enough and show how leveraging structural information provides considerable performance improvements. This particular example pinpoints the need for further research on the area of structural graph representation learning, together with the limitations of the previous state-of-the-art. We use the acquired knowledge as a starting point and inspiration for the research and development of three independent unsupervised structural graph representation learning methods: Structural Iterative Representation learning approach for Graph Nodes (SIR-GN), Structural Iterative Lexicographic Autoencoded Node Representation (SILA), and Sparse Structural Node Representation (SparseStruct). We show how each of our methods tackles specific limitations on the previous state-of-the-art on structural graph representation learning such as scalability, representation meaning, and lack of formal proof that guarantees the preservation of structural properties. We provide an extensive experimental section where we compare our three proposed methods to the current state-of-the-art on both connectivity-based and structure-based representation learning methods. Finally, in this dissertation, we look at extensions of the basic structural graph representation learning problem. We study the problem of temporal structural graph representation. We also provide a method for representation explainability.

scholarly journals GSSNN: Graph Smoothing Splines Neural Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 7007-7014
Author(s):  
Shichao Zhu ◽  
Lewei Zhou ◽  
Shirui Pan ◽  
Chuan Zhou ◽  
Guiying Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Representation Learning ◽  
Smoothing Splines ◽  
Graph Representation ◽  
Graph Classification ◽  
Topological Information ◽  
Graph Representations ◽  
The Arts ◽  
Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer from two limitations for graph representation learning. First, they exploit non-smoothing node features which may result in suboptimal embedding and degenerated performance for graph classification. Second, they only exploit neighbor information but ignore global topological knowledge. Aiming to overcome these limitations simultaneously, in this paper, we propose a novel, flexible, and end-to-end framework, Graph Smoothing Splines Neural Networks (GSSNN), for graph classification. By exploiting the smoothing splines, which are widely used to learn smoothing fitting function in regression, we develop an effective feature smoothing and enhancement module Scaled Smoothing Splines (S3) to learn graph embedding. To integrate global topological information, we design a novel scoring module, which exploits closeness, degree, as well as self-attention values, to select important node features as knots for smoothing splines. These knots can be potentially used for interpreting classification results. In extensive experiments on biological and social datasets, we demonstrate that our model achieves state-of-the-arts and GSSNN is superior in learning more robust graph representations. Furthermore, we show that S3 module is easily plugged into existing GNNs to improve their performance.

scholarly journals MolRep: A Deep Representation Learning Library for Molecular Property Prediction

2021 ◽  
Author(s):  
Jiahua Rao ◽  
Shuangjia Zheng ◽  
Ying Song ◽  
Jianwen Chen ◽  
Chengtao Li ◽  
...  
Keyword(s):  
State Of The Art ◽  
Source Code ◽  
Representation Learning ◽  
Supplementary Information ◽  
Data Sets ◽  
Supplementary Data ◽  
Property Prediction ◽  
Average Rank ◽  
Benchmark Data ◽  
Classification Tasks

AbstractSummaryRecently, novel representation learning algorithms have shown potential for predicting molecular properties. However, unified frameworks have not yet emerged for fairly measuring algorithmic progress, and experimental procedures of different representation models often lack rigorousness and are hardly reproducible. Herein, we have developed MolRep by unifying 16 state-of-the-art models across 4 popular molecular representations for application and comparison. Furthermore, we ran more than 12.5 million experiments to optimize hyperparameters for each method on 12 common benchmark data sets. As a result, CMPNN achieves the best results ranked the 1st in 5 out of 12 tasks with an average rank of 1.75. Relatively, ECC has good performance in classification tasks and MAT good for regression (both ranked 1st for 3 tasks) with an average rank of 2.71 and 2.6, respectively.AvailabilityThe source code is available at: https://github.com/biomed-AI/MolRepSupplementary informationSupplementary data are available online.

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