Scalable Multiplex Network Embedding

Zhang, Hongming; Qiu, Liwei; Yi, Lingling; Song, Yangqiu

doi:10.24963/ijcai.2018/428

Cited by 166 publications

(140 citation statements)

References 14 publications

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“…The compared methods include PMNE [22], MVE [30], MNE [43]. We denote the three methods of PMNE as PMNE(n), PMNE(r) and PMNE(c) respectively.…”

Section: Multiplex Heterogeneous Network Embedding Methodsmentioning

confidence: 99%

“…PMNE proposes three different methods to apply node2vec on multiplex networks. We denote their network aggregation algorithm, result aggregation algorithm, and layer co-analysis algorithm as PMNE(n), PMNE(r), and PMNE(c) respectively in accord with the denotations of MNE [43]. We use the codes from MNE's GitHub 13 .…”

Section: A23 Multiplex Heterogeneous Network Embedding Methodsmentioning

confidence: 99%

“…As for Alibaba dataset, we re-implemented this method on the Alibaba distributed cloud platform. • MNE [43]. MNE uses one common embedding and several additional embeddings of each edge type for each node, which are jointly learned by a unified network embedding model.…”

Section: A23 Multiplex Heterogeneous Network Embedding Methodsmentioning

confidence: 99%

“…Connection with Previous Work. We choose MNE [43], a recent representative work for MHEN, as the base model for multiplex heterogeneous networks to discuss the connection between our proposed model and previous work. In GATNE-T, we use the attention mechanism to capture the influential factors between different edge types.…”

Section: Transductive Model: Gatne-tmentioning

confidence: 99%

See 3 more Smart Citations

Representation Learning for Attributed Multiplex Heterogeneous Network

Cen

Zou

Zhang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

394

200

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Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba 1 . Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p ≪ 0.01, t−test) over previous state-of-the-art methods for link prediction.The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.

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“…The compared methods include PMNE [22], MVE [30], MNE [43]. We denote the three methods of PMNE as PMNE(n), PMNE(r) and PMNE(c) respectively.…”

Section: Multiplex Heterogeneous Network Embedding Methodsmentioning

confidence: 99%

Section: A23 Multiplex Heterogeneous Network Embedding Methodsmentioning

confidence: 99%

Section: A23 Multiplex Heterogeneous Network Embedding Methodsmentioning

confidence: 99%

Section: Transductive Model: Gatne-tmentioning

confidence: 99%

See 2 more Smart Citations

Representation Learning for Attributed Multiplex Heterogeneous Network

Cen

Zou

Zhang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

394

200

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“…Therefore, in this paper, we propose a multiplex word embedding (MWE) model, which can be easily extended to various relations between two words. A multiplex network embedding model was originally proposed for modeling multiple relations among people in a social network (Zhang et al, 2018). Interestingly, we found it also useful in capturing various relations among different words.…”

Section: Introductionmentioning

confidence: 96%

Multiplex Word Embeddings for Selectional Preference Acquisition

Zhang

Bai

Yan

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such representations is limited, where the same word might be functionalized separately under different syntactic relations. To address this limitation, one solution is to incorporate relational dependencies of different words into their embeddings. Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words. As a result, each word has a center embedding to represent its overall semantics, and several relational embeddings to represent its relational dependencies. Compared to existing models, our model can effectively distinguish words with respect to different relations without introducing unnecessary sparseness. Moreover, to accommodate various relations, we use a small dimension for relational embeddings and our model is able to keep their effectiveness. Experiments on selectional preference acquisition and word similarity demonstrate the effectiveness of the proposed model, and a further study of scalability also proves that our embeddings only need 1/20 of the original embedding size to achieve better performance.

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Multi-Net: A Scalable Multiplex Network Embedding Framework

Bagavathi

Krishnan

2018

Studies in Computational Intelligence

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Representation learning of networks has witnessed significant progress in recent times. Such representations have been effectively used for classic networkbased machine learning tasks like node classification, link prediction, and network alignment. However, very few methods focus on capturing representations for multiplex or multilayer networks, which are more accurate and detailed representations of complex networks. In this work, we propose Multi-Net a fast and scalable embedding technique for multiplex networks. Multi-Net, effectively maps nodes to a lower-dimensional space while preserving its neighborhood properties across all the layers. We utilize four random walk strategies in our unified network embedding model, thus making our approach more robust than existing state-of-the-art models. We demonstrate superior performance of Multi-Net on four real-world datasets from different domains. In particular, we highlight the uniqueness of Multi-Net by leveraging it for the complex task of network reconstruction.

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Scalable Multiplex Network Embedding

Cited by 166 publications

References 14 publications

Representation Learning for Attributed Multiplex Heterogeneous Network

Representation Learning for Attributed Multiplex Heterogeneous Network

Multiplex Word Embeddings for Selectional Preference Acquisition

Multi-Net: A Scalable Multiplex Network Embedding Framework

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