The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.

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An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks

Mathematics ◽

10.3390/math9151767 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1767

Author(s):

Xin Xu ◽

Yang Lu ◽

Yupeng Zhou ◽

Zhiguo Fu ◽

Yanjie Fu ◽

...

Keyword(s):

Random Walk ◽

Representation Learning ◽

Local Information ◽

Learning Framework ◽

Network Representation ◽

Label Node ◽

Label Information ◽

Classification Tasks ◽

Node Classification ◽

Low Dimensional

Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods have been proposed, where random walk strategy is one of the wildly utilized approaches. However, the existing random walk based methods have some challenges, including: 1. The insufficiency of explaining what network knowledge in the walking path-samplings; 2. The adverse effects caused by the mixture of different information in networks; 3. The poor generality of the methods with hyper-parameters on different networks. This paper proposes an information-explainable random walk based unsupervised network representation learning framework named Probabilistic Accepted Walk (PAW) to obtain network representation from the perspective of the stationary distribution of networks. In the framework, we design two stationary distributions based on nodes’ self-information and local-information of networks to guide our proposed random walk strategy to learn representational vectors of networks through sampling paths of nodes. Numerous experimental results demonstrated that the PAW could obtain more expressive representation than the other six widely used unsupervised network representation learning baselines on four real-world networks in single-label and multi-label node classification tasks.

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An Attributed Network Representation Learning Method Based on Biased Random Walk

Procedia Computer Science ◽

10.1016/j.procs.2020.06.088 ◽

2020 ◽

Vol 174 ◽

pp. 291-298

Author(s):

Wei Dou ◽

Weiyu Zhang ◽

Ziqiang Weng

Keyword(s):

Random Walk ◽

Representation Learning ◽

Learning Method ◽

Network Representation ◽

Biased Random Walk ◽

Attributed Network

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HIN_DRL: A random walk based dynamic network representation learning method for heterogeneous information networks

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113427 ◽

2020 ◽

Vol 158 ◽

pp. 113427

Author(s):

LU Meilian ◽

YE Danna

Keyword(s):

Random Walk ◽

Dynamic Network ◽

Representation Learning ◽

Information Networks ◽

Learning Method ◽

Heterogeneous Information ◽

Heterogeneous Information Networks ◽

Network Representation

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An Optimized Network Representation Learning Algorithm Using Multi-Relational Data

Mathematics ◽

10.3390/math7050460 ◽

2019 ◽

Vol 7 (5) ◽

pp. 460

Author(s):

Zhonglin Ye ◽

Haixing Zhao ◽

Ke Zhang ◽

Yu Zhu ◽

Zhaoyang Wang

Keyword(s):

Knowledge Representation ◽

Learning Algorithm ◽

Representation Learning ◽

Knowledge Bases ◽

Relational Data ◽

New Approach ◽

Network Representation ◽

Structural Relationships ◽

Low Dimensional

Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors. The purpose of network representation learning is to learn the structural relationships between network vertices. Knowledge representation learning is oriented to model the entities and relationships in knowledge bases. In this paper, we first introduce the idea of knowledge representation learning into network representation learning, namely, we propose a new approach to model the vertex triplet relationships based on DeepWalk without TransE. Consequently, we propose an optimized network representation learning algorithm using multi-relational data, MRNR, which introduces the multi-relational data between vertices into the procedures of network representation learning. Importantly, we adopted a kind of higher order transformation strategy to optimize the learnt network representation vectors. The purpose of MRNR is that multi-relational data (triplets) can effectively guide and constrain the procedures of network representation learning. The experimental results demonstrate that the proposed MRNR can learn the discriminative network representations, which show better performance on network classification, visualization, and case study tasks compared to the proposed baseline algorithms in this paper.

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Multi-View Network Representation Learning Algorithm Research

Algorithms ◽

10.3390/a12030062 ◽

2019 ◽

Vol 12 (3) ◽

pp. 62 ◽

Cited By ~ 1

Author(s):

Zhonglin Ye ◽

Haixing Zhao ◽

Ke Zhang ◽

Yu Zhu

Keyword(s):

Learning Algorithm ◽

Representation Learning ◽

Research Field ◽

Dimensional Representation ◽

Multi Scale ◽

Network Representation ◽

Excellent Research ◽

Network Feature ◽

The Matrix ◽

Low Dimensional

Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several excellent research achievements, including DeepWalk, node2vec and GraRep and so forth. We conduct our experiment on several real-world citation datasets and demonstrate that MVNR outperforms some new approaches using neural matrix factorization. Specifically, we demonstrate the efficiency of MVNR on network classification, visualization and link prediction tasks.

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