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.

scholarly journals Discrete Embedding for Latent Networks

2020 ◽  
Author(s):  
Hong Yang ◽  
Ling Chen ◽  
Minglong Lei ◽  
Lingfeng Niu ◽  
Chuan Zhou ◽  
...  
Keyword(s):  
Real World ◽  
Representation Learning ◽  
Mixed Integer ◽  
Information Cascades ◽  
Integer Optimization ◽  
Network Embedding ◽  
Structure Information ◽  
Proximity Matrix ◽  
Real World Datasets ◽  
Embedding Methods

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.

Attributed network representation learning via improved graph attention with robust negative sampling

2020 ◽  
Vol 51 (1) ◽  
pp. 416-426
Author(s):  
Huilian Fan ◽  
Yuanchang Zhong ◽  
Guangpu Zeng ◽  
Lili Sun
Keyword(s):  
Representation Learning ◽  
Network Representation ◽  
Attributed Network

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.

Deep attributed network representation learning of complex coupling and interaction

2021 ◽  
Vol 212 ◽  
pp. 106618 ◽  
Author(s):  
Zhao Li ◽  
Xin Wang ◽  
Jianxin Li ◽  
Qingpeng Zhang
Keyword(s):  
Representation Learning ◽  
Network Representation ◽  
Attributed Network

Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing

2021 ◽  
pp. 1-12
Author(s):  
Jia Chen ◽  
Ming Zhong ◽  
Jianxin Li ◽  
Dianhui Wang ◽  
Tieyun Qian ◽  
...  
Keyword(s):  
Representation Learning ◽  
Network Representation ◽  
Attributed Network

Attributed network representation learning via DeepWalk

2019 ◽  
Vol 23 (4) ◽  
pp. 877-893
Author(s):  
Hao Wei ◽  
Zhisong Pan ◽  
Guyu Hu ◽  
Guyu Hu ◽  
Haimin Yang ◽  
...  
Keyword(s):  
Representation Learning ◽  
Network Representation ◽  
Attributed Network

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 ANRL: Attributed Network Representation Learning via Deep Neural Networks

2018 ◽  
Author(s):  
Zhen Zhang ◽  
Hongxia Yang ◽  
Jiajun Bu ◽  
Sheng Zhou ◽  
Pinggang Yu ◽  
...  
Keyword(s):  
Network Structure ◽  
Link Prediction ◽  
Deep Neural Networks ◽  
User Behavior ◽  
Representation Learning ◽  
Network Representation ◽  
Node Attribute ◽  
Attributed Network ◽  
Attribute Information ◽  
Low Dimensional

Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network. Though network RL has been intensively studied, most existing works focus on either network structure or node attribute information. In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way. Specifically, we propose a neighbor enhancement autoencoder to model the node attribute information, which reconstructs its target neighbors instead of itself. To capture the network structure, attribute-aware skip-gram model is designed based on the attribute encoder to formulate the correlations between each node and its direct or indirect neighbors. We conduct extensive experiments on six real-world networks, including two social networks, two citation networks and two user behavior networks. The results empirically show that ANRL can achieve relatively significant gains in node classification and link prediction tasks.

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