Accelerated Attributed Network Embedding

Huang, Xiao; Li, Jundong; Hu, Xia

doi:10.1137/1.9781611974973.71

Cited by 264 publications

(167 citation statements)

References 37 publications

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“…Even if these approaches yield good results, they require tuning a lot of hyperparameters. Two methods are based on factorization approaches: GVNR-t [4], that extends GloVe [14], and AANE [7]. None of these methods learn documents and words embedding in the same space.…”

Section: Related Workmentioning

confidence: 99%

Document Network Projection in Pretrained Word Embedding Space

Gourru

Guille

Velcin

et al. 2020

Lecture Notes in Computer Science

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We present Regularized Linear Embedding (RLE), a novel method that projects a collection of linked documents (e.g. citation network) into a pretrained word embedding space. In addition to the textual content, we leverage a matrix of pairwise similarities providing complementary information (e.g., the network proximity of two documents in a citation graph). We first build a simple word vector average for each document, and we use the similarities to alter this average representation. The document representations can help to solve many information retrieval tasks, such as recommendation, classification and clustering. We demonstrate that our approach outperforms or matches existing document network embedding methods on node classification and link prediction tasks. Furthermore, we show that it helps identifying relevant keywords to describe document classes.

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Section: Related Workmentioning

confidence: 99%

Document Network Projection in Pretrained Word Embedding Space

Gourru

Guille

Velcin

et al. 2020

Lecture Notes in Computer Science

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“…Network Embedding. The goal of network embedding is to learn a low dimensional representation for each node of a network while preserving the network structure and various properties such as attributes related to nodes [3,13,14] and edges [11]. The learned embeddings are then used for general downstream tasks, such as node classification [1], link prediction [22,36], and clustering [33].…”

Section: Related Workmentioning

confidence: 99%

Task-Guided Pair Embedding in Heterogeneous Network

Park

Kim

Zhu

et al. 2019

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

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Many real-world tasks solved by heterogeneous network embedding methods can be cast as modeling the likelihood of a pairwise relationship between two nodes. For example, the goal of author identification task is to model the likelihood of a paper being written by an author (paper-author pairwise relationship). Existing taskguided embedding methods are node-centric in that they simply measure the similarity between the node embeddings to compute the likelihood of a pairwise relationship between two nodes. However, we claim that for task-guided embeddings, it is crucial to focus on directly modeling the pairwise relationship. In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e.g., paper-author relationship in author identification). To this end, we 1) propose to learn a pair embedding under the guidance of its associated context path, i.e., a sequence of nodes between the pair, and 2) devise the pair validity classifier to distinguish whether the pair is valid with respect to the specific task at hand. By introducing pair embeddings that capture the semantics behind the pairwise relationships, we are able to learn the fine-grained pairwise relationship between two nodes, which is paramount for task-guided embedding methods. Extensive experiments on author identification task demonstrate that TaPEm outperforms the state-of-the-art methods, especially for authors with few publication records.

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“…Recently, several methods have been proposed to embed an attributed network, in which each node is associated with an attribute vector [11,12,28,42,43]. Their key idea is to integrate a dimension reduction component of attribute vectors into a network embedding framework to leverage the complementary information in node attributes and network structure.…”

Section: Related Workmentioning

confidence: 99%

Co-Regularized Deep Multi-Network Embedding

Chang²,

Liu

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further re ned node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is exible. It allows di erent networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts di erent spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the e ectiveness of our method.

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Accelerated Attributed Network Embedding

Cited by 264 publications

References 37 publications

Document Network Projection in Pretrained Word Embedding Space

Document Network Projection in Pretrained Word Embedding Space

Task-Guided Pair Embedding in Heterogeneous Network

Co-Regularized Deep Multi-Network Embedding

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