node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching

Jin, Di; Heimann, Mark; Rossi, Ryan A.; Koutra, Danai

doi:10.1007/978-3-030-46150-8_29

Cited by 22 publications

(21 citation statements)

References 29 publications

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“…Then, link prediction is obtained using fully connected neural networks by taking the embeddings of source and target nodes as inputs. 4) Node2Bits [44] is a method for modeling the interactions among users in heterogeneous web networks for personalization and recommendations of web services. It represents multi-dimensional features of node contexts with binary hashcodes, and predicts whether two users correspond to the same entity (has a link) by considering temporal dimension and dynamic attributes associated with each user.…”

Section: Methodsmentioning

confidence: 99%

Learning Traffic Network Embeddings for Predicting Congestion Propagation

Sun

Jiang

Lam

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: Methodsmentioning

confidence: 99%

Learning Traffic Network Embeddings for Predicting Congestion Propagation

Sun

Jiang

Lam

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…Usually, language models such as Skip-Gram [79] are applied on generated random walks to map the similarities into embedding vectors [39], [65], [77], [78]. However, some different mapping mechanisms are also employed such as the SimHash [80] used in NODE2BITS [67].…”

Section: Shallow Models Using Random Walksmentioning

confidence: 99%

“…And embeddings are learned via SGNS on the generated sequences. NODE2BITS [67]. NODE2BITS is designed for entity resolution on temporal networks.…”

Section: Structural Feature-based Random Walksmentioning

confidence: 99%

“…In specific, RolX [38], RIDεRs [58], GraphWave [59] and SEGK [56] belong to the low-rank matrix factorization. struc2vec [39], struc2gauss [64], Role2Vec [65] and NODE2BITS [67] are all based on random walk. DRNE [50], GraLSP [70], GAS [68] and RESD [69] pertain to the scope of deep learning.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Generally speaking, the methods based on deep learning are relatively efficient compared to others, especially for the methods that need higher-order features. GAS [68], RESD [69] and NODE2BITS [67] are the three most efficient methods and RolX [38] also has competitive results. struc2gauss [64], SEGK [56] and Role2vec [65] cost more time to learn the node embeddings since they need to compute higher-order features, e.g., motif.…”

Section: Efficiency Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Role-Oriented Network Embedding

Jiao¹,

Guo²,

Pan³

et al. 2021

Preprint

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Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in various of graph mining tasks including link prediction and node clustering and classification. A wide variety of NE methods focus on the proximity of networks. They learn community-oriented embedding for each node, where the corresponding representations are similar if two nodes are closer to each other in the network. Meanwhile, there is another type of structural similarity, i.e., role-based similarity, which is usually complementary and completely different from the proximity. In order to preserve the role-based structural similarity, the problem of role-oriented NE is raised. However, compared to community-oriented NE problem, there are only a few role-oriented embedding approaches proposed recently. Although less explored, considering the importance of roles in analyzing networks and many applications that role-oriented NE can shed light on, it is necessary and timely to provide a comprehensive overview of existing role-oriented NE methods. In this review, we first clarify the differences between community-oriented and role-oriented network embedding. Afterwards, we propose a general framework for understanding role-oriented NE and a two-level categorization to better classify existing methods. Then, we select some representative methods according to the proposed categorization and briefly introduce them by discussing their motivation, development and differences. Moreover, we conduct comprehensive experiments to empirically evaluate these methods on a variety of role-related tasks including node classification and clustering (role discovery), top-k similarity search and visualization using some widely used synthetic and real-world datasets. Finally, we further discuss the research trend of role-oriented NE from the perspective of applications and point out some potential future directions. The source code and datasets used in the experiments are available at Github.

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Hyperbolic node embedding for temporal networks

Wang

Huang

et al. 2021

Data Min Knowl Disc

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node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching

Cited by 22 publications

References 29 publications

Learning Traffic Network Embeddings for Predicting Congestion Propagation

Learning Traffic Network Embeddings for Predicting Congestion Propagation

A Survey on Role-Oriented Network Embedding

Hyperbolic node embedding for temporal networks

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