Graph Embedding Framework Based on Adversarial and Random Walk Regularization

Dou, Wei; Zhang, Weiyu; Weng, Ziqiang; Xia, Zhongxiu

doi:10.1109/access.2020.3047116

Cited by 6 publications

(10 citation statements)

References 32 publications

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“…The W-MetaGraph2Vec algorithm uses a random-walk mechanism based on a topic-driven metagraph to guide the generation of heterogeneous neighborhoods of nodes [19]. The ARWR-GE algorithm preserves the high-order neighbor information of nodes through a random walk and uses adversarial learning to obtain node embedding [20]. The HIN-DRL algorithm adopts a random-walk-based dynamic representation learning to learn the embedding of nodes under different timestamps [21].…”

Section: Related Workmentioning

confidence: 99%

HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

2023

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Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.

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Section: Related Workmentioning

confidence: 99%

HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

2023

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“…Jiahua et al [ 23 ] proposed a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data and the study is a step forward towards improved real-time pollen allergy surveillance from social media with state-of-art technology. Wei et al [ 24 ] proposed a novel graph embedding framework, Adversarial and Random Walk Regularized Graph Embedding (ARWR-GE), and the results demonstrate that the framework achieves better performance than state-of-the-art graph embedding algorithms.Similarly, different index construction methods support different datasets.But there is a lack of researches to manage these embedding models and index construction methods in application scenarios.…”

Section: Related Workmentioning

confidence: 99%

A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration

Zhang

Sheng

Liu

et al. 2022

Health Inf Sci Syst

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Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.

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“…DRRW [23] analyzes the convergence of random path and proposes an exploration score to guide the path toward less-visited nodes for better distribution learning. Extended studies further aim at learning node embeddings in attributed networks, in which ANRL [24], RWR-GAE [25], and ARWR-GE [26] are random walk-based approaches that also incorporate the Skip-gram model as a component for the graph structure preservation. On the other hand, some methods such as DANE [27], GraphRNA [28], and wGCN [29] utilize the random walk to extract the graph structure and help the representation learning via random path and co-currency.…”

Section: Related Workmentioning

confidence: 99%

Toward an Adaptive Skip-Gram Model for Network Representation Learning

Hsieh

2022

IEEE Access

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The random walk process on network data is a widely-used approach for network representation learning. However, we argue that the sampling of node sequences and the subsampling for the Skip-gram's contexts have two drawbacks. One is less possible to precisely find the most correlated context nodes for every central node with only uniform graph search. The other is not easily controlled due to the expensive cost of hyperparameter tuning. Such two drawbacks lead to higher training cost and lower accuracy due to abundant and irrelevant samples. To solve these problems, we compute the adaptive probability of random walk based on Personalized PageRank (PPR), and propose an Adaptive SKip-gram (ASK) model without using complicated sampling process and negative sampling. We utilize k-most important neighbors for positive samples selection, and attach their corresponding PPR probability into the objective function. Based on benchmark datasets with three citation networks and three social networks, we demonstrate the improvement of our ASK model for network representation learning in tasks of link prediction, node classification, and embedding visualization. The results achieve more effective performance and efficient learning time.

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Graph Embedding Framework Based on Adversarial and Random Walk Regularization

Cited by 6 publications

References 32 publications

HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration

Toward an Adaptive Skip-Gram Model for Network Representation Learning

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