Predictive Network Representation Learning for Link Prediction

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance. Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science.

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Attributed Network Representation Learning Approaches for Link Prediction

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) ◽

10.1109/asonam.2018.8508500 ◽

2018 ◽

Author(s):

Farzan Masrour ◽

Pang-Ning Tan ◽

Abdol-Hossein Esfahanian ◽

Courtl VanDam

Keyword(s):

Link Prediction ◽

Representation Learning ◽

Learning Approaches ◽

Network Representation ◽

Attributed Network

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Structural Hierarchy-Enhanced Network Representation Learning

Applied Sciences ◽

10.3390/app10207214 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7214

Author(s):

Cheng-Te Li ◽

Hong-Yu Lin

Keyword(s):

Network Topology ◽

Link Prediction ◽

Main Idea ◽

Representation Learning ◽

Coarse Grained ◽

Community Knowledge ◽

Structural Hierarchy ◽

Network Representation ◽

Node Classification ◽

Node Embeddings

Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding space, nor model coarse-grained community knowledge hidden behind the network topology. In this paper, we propose a novel NRL framework, Structural Hierarchy Enhancement (SHE), to deal with such two issues. The main idea is to construct a structural hierarchy from the network based on community detection, and to utilize such a hierarchy to perform level-wise NRL. In addition, lower-level node embeddings are passed to higher-level ones so that community knowledge can be aware of in NRL. Experiments conducted on benchmark network datasets show that SHE can significantly boost the performance of NRL in both tasks of NC and LP, compared to other hierarchical NRL methods.

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Feature Hashing for Network Representation Learning

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/390 ◽

2018 ◽

Cited By ~ 2

Author(s):

Qixiang Wang ◽

Shanfeng Wang ◽

Maoguo Gong ◽

Yue Wu

Keyword(s):

Link Prediction ◽

Feature Space ◽

Representation Learning ◽

Learning Approaches ◽

Network Representation ◽

Proximity Matrix ◽

Low Dimensional ◽

Vector Representations ◽

Feature Hashing ◽

Node Embeddings

The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build a proximity matrix, then introduce a set of T different hash functions into feature hashing to generate uniformly distributed vector representations of nodes from the proximity matrix. Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains.

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A Survey of Network Representation Learning Methods for Link Prediction in Biological Network

Current Pharmaceutical Design ◽

10.2174/1381612826666200116145057 ◽

2020 ◽

Vol 26 (26) ◽

pp. 3076-3084 ◽

Cited By ~ 1

Author(s):

Jiajie Peng ◽

Guilin Lu ◽

Xuequn Shang

Keyword(s):

Network Analysis ◽

Biological Networks ◽

Link Prediction ◽

Biological Network ◽

Representation Learning ◽

Learning Methods ◽

Future Directions ◽

Value Network ◽

Network Representation ◽

Important Practical Application

Background: Networks are powerful resources for describing complex systems. Link prediction is an important issue in network analysis and has important practical application value. Network representation learning has proven to be useful for network analysis, especially for link prediction tasks. Objective: To review the application of network representation learning on link prediction in a biological network, we summarize recent methods for link prediction in a biological network and discuss the application and significance of network representation learning in link prediction task. Method & Results: We first introduce the widely used link prediction algorithms, then briefly introduce the development of network representation learning methods, focusing on a few widely used methods, and their application in biological network link prediction. Existing studies demonstrate that using network representation learning to predict links in biological networks can achieve better performance. In the end, some possible future directions have been discussed.

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Link Prediction Based on Deep Convolutional Neural Network

Information ◽

10.3390/info10050172 ◽

2019 ◽

Vol 10 (5) ◽

pp. 172

Author(s):

Wentao Wang ◽

Lintao Wu ◽

Ye Huang ◽

Hao Wang ◽

Rongbo Zhu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Link Prediction ◽

Prediction Method ◽

Representation Learning ◽

Deep Convolutional Neural Network ◽

Published Data ◽

Neighborhood Structure ◽

Structure Information ◽

Network Representation

In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction.

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ANRL: Attributed Network Representation Learning via Deep Neural Networks

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/438 ◽

2018 ◽

Cited By ~ 31

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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A Drug Target Interaction Prediction Based on LINE-RF Learning

Current Bioinformatics ◽

10.2174/1574893615666191227092453 ◽

2020 ◽

Vol 15 (7) ◽

pp. 750-757

Author(s):

Jihong Wang ◽

Yue Shi ◽

Xiaodan Wang ◽

Huiyou Chang

Keyword(s):

Network Topology ◽

Drug Target ◽

Large Scale ◽

Representation Learning ◽

New Drugs ◽

Combination Method ◽

Learning Methods ◽

Network Representation ◽

On Line ◽

Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

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W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

Intelligent Data Analysis ◽

10.3233/ida-205168 ◽

2021 ◽

Vol 25 (3) ◽

pp. 711-738

Author(s):

Phu Pham ◽

Phuc Do

Keyword(s):

Link Prediction ◽

Representation Learning ◽

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Learning Framework ◽

Novel Approach ◽

Proposed Model ◽

Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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

IEEE Access ◽

10.1109/access.2020.3044367 ◽

2020 ◽

Vol 8 ◽

pp. 222956-222965

Author(s):

Dong Liu ◽

Qinpeng Li ◽

Yan Ru ◽

Jun Zhang

Keyword(s):

Random Walk ◽

Learning Algorithm ◽

Representation Learning ◽

Network Representation

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