scholarly journals Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 186
Author(s):  
Hanlin Sun ◽  
Wei Jie ◽  
Jonathan Loo ◽  
Liang Chen ◽  
Zhongmin Wang ◽  
...  
Keyword(s):  
Game Theory ◽  
Community Structure ◽  
State Of The Art ◽  
Representation Learning ◽  
Network Embedding ◽  
Network Representation ◽  
Low Dimension ◽  
Community Information ◽  
Model Community ◽  
Series Of Experiments

Presently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving network topology properties in learned low-dimension representations. Community structure is a prominent characteristic of complex networks and thus should be well maintained. However, the difficulty lies in the fact that the properties of community structure are multivariate and complicated; therefore, it is insufficient to model community structure using a predefined model, the way that is popular in most state-of-the-art network embedding algorithms explicitly considering community structure preservation. In this paper, we introduce a multi-process parallel framework for network embedding that is enhanced by found partial community information and can preserve community properties well. We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. A series of experiments are conducted to evaluate the performance of our methods and six state-of-the-art algorithms. The results demonstrate that our methods can effectively preserve community properties of networks in their low-dimension representations. Specifically, compared to the involved baselines, our algorithms behave the best and are the runners-up on networks with high overlapping diversity and density.

Graph Community Infomax

2022 ◽  
Vol 16 (3) ◽  
pp. 1-21
Author(s):  
Heli Sun ◽  
Yang Li ◽  
Bing Lv ◽  
Wujie Yan ◽  
Liang He ◽  
...  
Keyword(s):  
Mutual Information ◽  
Representation Learning ◽  
Graph Representation ◽  
Network Embedding ◽  
Adversarial Network ◽  
Low Dimension ◽  
Community Information ◽  
Model Graph ◽  
Embedding Methods ◽  
Attributed Networks

Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same time. An autoencoder is applied to learn the embedding vectors for nodes and reconstruct the adjacency matrix, and a discriminator is used to maximize the mutual information between nodes and communities. Experiments on several real-world and synthetic networks have shown that GCI outperforms various network embedding methods on community detection tasks.

Network Embedding on Hierarchical Community Structure Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Guojie Song ◽  
Yun Wang ◽  
Lun Du ◽  
Yi Li ◽  
Junshan Wang
Keyword(s):  
Community Structure ◽  
Structural Information ◽  
Spherical Surface ◽  
Network Embedding ◽  
The Galaxy ◽  
Community Information ◽  
The Hierarchical Structure ◽  
Network Properties ◽  
Multi Class Classification ◽  
Low Dimensional

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.

scholarly journals Network Representation Learning Guided by Partial Community Structure

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 46665-46681
Author(s):  
Hanlin Sun ◽  
Wei Jie ◽  
Zhongmin Wang ◽  
Hai Wang ◽  
Sugang Ma
Keyword(s):  
Community Structure ◽  
Representation Learning ◽  
Network Representation

scholarly journals Learning Network Embedding with Community Structural Information

2019 ◽  
Author(s):  
Yu Li ◽  
Ying Wang ◽  
Tingting Zhang ◽  
Jiawei Zhang ◽  
Yi Chang
Keyword(s):  
Community Structure ◽  
Link Prediction ◽  
Structural Information ◽  
Representation Learning ◽  
Network Embedding ◽  
Learning Network ◽  
Optimization Framework ◽  
Vertex Representation ◽  
Low Dimensional ◽  
Embedding Methods

Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification.

scholarly journals Galaxy Network Embedding: A Hierarchical Community Structure Preserving Approach

2018 ◽  
Author(s):  
Lun Du ◽  
Zhicong Lu ◽  
Yun Wang ◽  
Guojie Song ◽  
Yiming Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Structural Information ◽  
Spherical Surface ◽  
Network Embedding ◽  
Structure Preserving ◽  
The Galaxy ◽  
Community Information ◽  
The Hierarchical Structure ◽  
Multi Class Classification ◽  
Low Dimensional

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification and network visualization. The source code of GNE is available online.

scholarly journals Confusion Graph: Detecting Confusion Communities in Large Scale Image Classification

2017 ◽  
Author(s):  
Ruochun Jin ◽  
Yong Dou ◽  
Yueqing Wang ◽  
Xin Niu
Keyword(s):  
Community Structure ◽  
Image Classification ◽  
Large Scale ◽  
State Of The Art ◽  
Classification Models ◽  
The Social ◽  
Community Information ◽  
Deep Cnn ◽  
Almost All ◽  
Classification Database

For deep CNN-based image classification models, we observe that confusions between classes with high visual similarity are much stronger than those where classes are visually dissimilar. With these unbalanced confusions, classes can be organized in communities, which is similar to cliques of people in the social network. Based on this, we propose a graph-based tool named "confusion graph" to quantify these confusions and further reveal the community structure inside the database. With this community structure, we can diagnose the model's weaknesses and improve the classification accuracy using specialized expert sub-nets, which is comparable to other state-of-the-art techniques. Utilizing this community information, we can also employ pre-trained models to automatically identify mislabeled images in the large scale database. With our method, researchers just need to manually check approximate 3% of the ILSVRC2012 classification database to locate almost all mislabeled samples.

scholarly journals Correlational Neural Networks

2016 ◽  
Vol 28 (2) ◽  
pp. 257-285 ◽  
Author(s):  
Sarath Chandar ◽  
Mitesh M. Khapra ◽  
Hugo Larochelle ◽  
Balaraman Ravindran
Keyword(s):  
Canonical Correlation ◽  
State Of The Art ◽  
Representation Learning ◽  
Advantages And Disadvantages ◽  
Common Representation ◽  
Series Of Experiments ◽  
The Common ◽  
Cross Language ◽  
Joint Representation ◽  
Better Than

Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)–based approaches and autoencoder (AE)–based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

scholarly journals TAXOGAN: Hierarchical Network Representation Learning via Taxonomy Guided Generative Adversarial Networks (Extended Abstract)

2021 ◽  
Author(s):  
Carl Yang ◽  
Jieyu Zhang ◽  
Jiawei Han
Keyword(s):  
Case Studies ◽  
Hierarchical Structures ◽  
Representation Learning ◽  
Generative Adversarial Networks ◽  
Network Embedding ◽  
Hierarchical Network ◽  
Network Representation ◽  
Adversarial Networks ◽  
Node Labels ◽  
The Given

Network representation learning aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. Comprehensive experiments and case studies demonstrate the utility of TAXOGAN.

scholarly journals Low-Bit Quantization for Attributed Network Representation Learning

2019 ◽  
Author(s):  
Hong Yang ◽  
Shirui Pan ◽  
Ling Chen ◽  
Chuan Zhou ◽  
Peng Zhang
Keyword(s):  
Representation Learning ◽  
Significant Loss ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Network Embedding ◽  
Learning Function ◽  
Network Representation ◽  
Binary Coding ◽  
Attributed Network ◽  
Effective Network

Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.

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