Learning heterogeneous information network embeddings via relational triplet network

Gao, Xiyue; Zhan, Zexing; Yang, Shuai

doi:10.1016/j.neucom.2020.06.043

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…In heterogeneous network platform, there are different types of links between nodes, which represent different kinds of relations and contain rich semantic information. How to calculate the similarity between nodes is an important problem in the process of extracting the relation information of heterogeneous networks [28][29][30]. With the rapid development of artificial intelligence machine learning in recent years, many new technologies have emerged.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

Wang

2022

Mobile Information Systems

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Music service is one of the diversified network services offered by people in the Internet era. Various music websites provide many tracks to meet people’s music needs. Hundreds of millions of music of various genres at home and abroad, and there is a severe problem of information asymmetry between users and music. As a branch of the information filtering system, the recommendation system can predict users’ preferences, increase flow, and drive consumption. A personalized music recommendation system can effectively provide people with a list of favorite tracks. Recently, many researchers have paid attention to heterogeneous networks because of their rich semantics information. Research has confirmed that rich relationship information in heterogeneous networks can improve the recommendation effect. Therefore, under the platform of a heterogeneous network, this paper divides the digraph set of track characteristics into several clusters with maximum heterogeneity, which makes the digraph of track characteristics in each cluster isomorphic to the maximum extent. When matching similarity, only searching in the cluster with the highest similarity to the target user can match a sufficient amount of applicable tracks, thus improving the efficiency of music recommendations to users. Experimental results show that the proposed algorithm has a high recall, precision, and F1 and can recommend personalized track lists to users to meet their music needs.

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Section: Related Workmentioning

confidence: 99%

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

Wang

2022

Mobile Information Systems

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“…In this method, one triplet network focused on negative attribute features and the other triplet network paid attention on negative visual features to ensure that the data information is fully utilized. Gao et al [33] proposed a new heterogeneous information network embedding algorithm. In the data sampling phase, a metaschema-based random walk was performed to extract semihard quadruplets based on the node type and its degree.…”

Section: Triple Networkmentioning

confidence: 99%

Few-Shot Learning-Based Network Intrusion Detection through an Enhanced Parallelized Triplet Network

Tian

Wang

Fang

et al. 2022

Security and Communication Networks

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Network intrusion detection is one of the critical techniques to enhance cybersecurity. Several few-shot learning-based methods have recently been proposed to alleviate the dependence on large training samples in many supervised learning methods. However, it is still a challenge to achieve real-time higher-accuracy intrusion detection which is an essential requirement for high-speed network security. In this study, we propose a novel few-shot learning-based network intrusion detection method to address this challenge. Specifically, we improve the detection accuracy and real-time processing speed simultaneously in the metric procedure via two mechanisms: (i) we utilize a hard sample selection scheme as a refining stage of our triplet network model training to increase the detection accuracy; and (ii) we design a lightweight embedding network and parallelize the metric feature extraction process to achieve real-time analysis speed. To evaluate the proposed method, we construct few-shot learning-based datasets by using two real and heterogeneous network traffic intrusion detection data sources. Extensive results demonstrate that our method outperforms the state-of-the-art methods in terms of real-time performance and high detection accuracy of malicious samples.

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“…Embedding methods, which are effective for extracting features from graph structured data, have broadly attracted significant attention in the literature and different embedding methods such as DeepWalk [23], Line [25], node2vec [26], graph-GAN [27], NetMF [29], ATP [24], BoostNE [30], DWNS [22], and NERD [28] have been proposed for homogeneous graphs. In addition, some embedding methods have been proposed that target the heterogeneous graphs such as HeGAN [31], RTN [32], and HAN [33]. Some methods such as SIDE [34], CSNE [35], and nSNE [36] perform feature representation learning in signed graphs where there are two types of links (i.e., positive and negative) (In this paper, we focus only on the unsigned homogeneous graphs and the related embedding methods).…”

Section: Introductionmentioning

confidence: 99%

On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs

Hamedani

Kim

2020

Applied Sciences

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One of the important tasks in a graph is to compute the similarity between two nodes; link-based similarity measures (in short, similarity measures) are well-known and conventional techniques for this task that exploit the relations between nodes (i.e., links) in the graph. Graph embedding methods (in short, embedding methods) convert nodes in a graph into vectors in a low-dimensional space by preserving social relations among nodes in the original graph. Instead of applying a similarity measure to the graph to compute the similarity between nodes a and b, we can consider the proximity between corresponding vectors of a and b obtained by an embedding method as the similarity between a and b. Although embedding methods have been analyzed in a wide range of machine learning tasks such as link prediction and node classification, they are not investigated in terms of similarity computation of nodes. In this paper, we investigate both effectiveness and efficiency of embedding methods in the task of similarity computation of nodes by comparing them with those of similarity measures. To the best of our knowledge, this is the first work that examines the application of embedding methods in this special task. Based on the results of our extensive experiments with five well-known and publicly available datasets, we found the following observations for embedding methods: (1) with all datasets, they show less effectiveness than similarity measures except for one dataset, (2) they underperform similarity measures with all datasets in terms of efficiency except for one dataset, (3) they have more parameters than similarity measures, thereby leading to a time-consuming parameter tuning process, (4) increasing the number of dimensions does not necessarily improve their effectiveness in computing the similarity of nodes.

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Learning heterogeneous information network embeddings via relational triplet network

Cited by 9 publications

References 25 publications

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

Few-Shot Learning-Based Network Intrusion Detection through an Enhanced Parallelized Triplet Network

On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs

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