Semi-Supervised Network Embedding

Li, Chaozhuo; Li, Zhoujun; Wang, Senzhang; Yang, Yang; Zhang, Xiaoming; Zhou, Jianshe

doi:10.1007/978-3-319-55753-3_9

Cited by 26 publications

(20 citation statements)

References 18 publications

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“…Factorization strategies vary across different algo- Unsupervised Social Dim. [31], [32], [33] DeepWalk [6] LINE [1] GraRep [26] DNGR [9] SDNE [19] node2vec [34] HOPE [35] APP [36] M-NMF [28] GraphGAN [37] struct2vec [38] GraphWave [39] SNS [40] DP [41] HARP [42] TADW [7] HSCA [8] pRBM [29] UPP-SNE [43] PPNE [44] Semi-supervised DDRW [45] MMDW [46] TLINE [47] GENE [48] SemiNE [49] TriDNR [50] LDE [51] DMF [8] Planetoid [52] LANE [30] rithms according to their objectives. For example, in the Modularity Maximization method [31], eigen decomposition is performed on the modularity matrix to learn community indicative vertex representations [53]; in the TADW algorithm [7], inductive matrix factorization [54] is carried out on the vertexcontext matrix to simultaneously preserve vertex textual features and network structure in the learning of vertex representations.…”

Section: Categorizationmentioning

confidence: 99%

“…As the extensions of the structure only preserving version, algorithms like DDRW [45], GENE [48] and SemiNE [49] incorporate vertex labels with network structure to harness representation learning, PPNE [44] imports vertex attributes, and Tri-DNR [50] enforces the model with both vertex labels and attributes. As these models can be trained in an online manner, they have great potential to scale up.…”

Section: Categorizationmentioning

confidence: 99%

“…DeepWalk [6], node2vec [34], APP [36], DDRW [45], GENE [48], TriDNR [50], UPP-SNE [43], struct2vec [38], SNS [40], PPNE [44], SemiNE [49] relatively efficient only capture local structure Edge Modeling LINE [1], TLINE [47], LDE [51], pRBM [29], GraphGAN [37] efficient only capture local structure Deep Learning DNGR [9], SDNE [19] capture non-linearity high time cost…”

Section: Random Walkmentioning

confidence: 99%

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Network Representation Learning: A Survey

Zhang

Yin

Zhu

2020

IEEE Trans. Big Data

520

318

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With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.Definition 2 (First-order Proximity). The first-order proximity is the local pairwise proximity between two connected vertices [1]. For each vertex pair (v i , v j ), if (v i , v j ) ∈ E, the first-order proximity between v i and v j is w ij ; otherwise, the first-order proximity between v i and v j is 0. The first-order proximity captures the direct neighbor relationships between vertices.Definition 3 (Second-order Proximity and High-order Proximity). The second-order proximity captures the 2-step relations between each pair of vertices [1]. For each vertex pair (v i , v j ), the second order proximity is determined by the number of common neighbors shared by the two vertices, which can also be measured by the 2-step transition probability from v i to v j equivalently. Compared with the second-order proximity, the highorder proximity [26] captures more global structure, which explores k-step (k ≥ 3) relations between each pair of vertices. For each vertex pair (v i , v j ), the higherorder proximity is measured by the k-step (k ≥ 3) transition probability from vertex v i to vertex v j , which can also be reflected by the number of k-step (k ≥ 3) paths from v i to v j . The second-order and high-order proximity capture the similarity between a pair of, indirectly connected, vertices with similar structural contex...

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Section: Categorizationmentioning

confidence: 99%

Section: Categorizationmentioning

confidence: 99%

Section: Random Walkmentioning

confidence: 99%

See 1 more Smart Citation

Network Representation Learning: A Survey

Zhang

Yin

Zhu

2020

IEEE Trans. Big Data

520

318

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show abstract

“…Besides the network topology information, several works focused on incorporating the side information as the complementary to improve the quality of node embeddings [11,15,17,23,28,33]. Tu et al [28] extended the DeepWalk model to a max-margin extension to incorporate few available labels.…”

Section: Related Workmentioning

confidence: 99%

“…Tu et al [28] extended the DeepWalk model to a max-margin extension to incorporate few available labels. Li et al [15] designed a multi-layer perceptron based model to perform semi-supervised network embedding. Yang et al proposed TADW [33], a matrix factorization based model to fuse node attributes (text features) into the embedding process.…”

Section: Related Workmentioning

confidence: 99%

Multi-Hot Compact Network Embedding

Zheng

Wang

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Self Cite

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Network embedding, as a promising way of the network representation learning, is capable of supporting various subsequent network mining and analysis tasks, and has attracted growing research interests recently. Traditional approaches assign each node with an independent continuous vector, which will cause memory overhead for large networks. In this paper we propose a novel multi-hot compact network embedding framework to effectively reduce memory cost by learning partially shared embeddings. The insight is that a node embedding vector is composed of several basis vectors according to a multi-hot index vector. The basis vectors are shared by different nodes, which can significantly reduce the number of continuous vectors while maintain similar data representation ability. Specifically, we propose a MCNE p model to learn compact embeddings from pre-learned node features. A novel component named compressor is integrated into MCNE p to tackle the challenge that popular back-propagation optimization cannot propagate loss through discrete samples. We further propose an endto-end model MCNE t to learn compact embeddings from the input network directly. Empirically, we evaluate the proposed models over four real network datasets, and the results demonstrate that our proposals can save about 90% of memory cost of network embeddings without significantly performance decline.

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AI Algorithms in Networks

2022

Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

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Semi-Supervised Network Embedding

Cited by 26 publications

References 18 publications

Network Representation Learning: A Survey

Network Representation Learning: A Survey

Multi-Hot Compact Network Embedding

AI Algorithms in Networks

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