Heterogeneous graph-based joint representation learning for users and POIs in location-based social network

Qiao, Yuanhua; Luo, Xiangyang; Li, Chenliang; Tian, Hechan; Ma, Jiangtao

doi:10.1016/j.ipm.2019.102151

Cited by 57 publications

(18 citation statements)

References 8 publications

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“…In the present study, two different strategies were used to construct an autoencoder, and the joint multimodal representation strategy was better than the early fusion autoencoder strategy. This was consistent with a previous study, in which it was believed that the joint multimodal representation strategy may alleviate the problems associated with the fusion of original data (31). The present study identified two risk subgroups of ESCC with significantly different survival by using the joint multimodal representation-based classification model.…”

Section: Discussionsupporting

confidence: 92%

A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

Zhang

et al. 2020

Oncol Lett

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Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancer types with a poor prognosis due to the lack of symptoms in the early stages and a delayed diagnosis. The present study aimed to identify the risk factors significantly associated with prognosis and to search for novel effective diagnostic modalities for patients with early-stage ESCC. mRNA and methylation data of patients with ESCC and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database, and the representation features were screened using deep learning autoencoder. The univariate Cox regression model was used to select the prognosis-related features from the representation features. K-means clustering was used to cluster the TCGA samples. Support vector machine classifier was constructed based on the top 75 features mostly associated with the risk subgroups obtained from K-means clustering. Two ArrayExpress datasets were used to verify the reliability of the obtained risk subgroups. The differentially expressed genes and methylation genes (DEGs and DMGs) between the risk subgroups were analyzed, and pathway enrichment analysis was performed. A total of 500 representation features were produced. Using K-means clustering, the TCGA samples were clustered into two risk subgroups with significantly different overall survival rates. Joint multimodal representation strategy, which showed a good model fitness (C-index=0.760), outperformed early-fusion autoencoder strategy. The joint representation learning-based classification model had good robustness. A total of 1,107 DEGs and 199 DMGs were screened out between the two risk subgroups. The DEGs were involved in 70 pathways, the majority of which were correlated with metastasis and proliferation of various cancer types, including cytokine-cytokine receptor interaction, cell adhesion molecules PPAR signaling pathway, pathways in cancer, transcriptional misregulation in cancer and ECM-receptor interaction pathways. The two survival subgroups obtained via the joint representation learning-based model had good robustness, and had prognostic significance for patients with ESCC.

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Section: Discussionsupporting

confidence: 92%

A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

Zhang

et al. 2020

Oncol Lett

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“…Qiao et al. ( 2020 ) model location-based social networks by using heterogeneous graphs which describe representations of users, points of interests, and temporal information. Gbadouissa et al.…”

Section: Related Workmentioning

confidence: 99%

Structural textile pattern recognition and processing based on hypergraphs

et al. 2021

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The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing. To facilitate the clustering and search, we introduce an approach for recognising similar weaving patterns based on their structures for textile archives. We first represent textile structures using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs. Then, the resulting multisets are clustered using various distance measures and various clustering algorithms (K-Means for simplicity and hierarchical agglomerative algorithms for precision). We evaluate the different variants of our approach experimentally, showing that this can be implemented efficiently (meaning it has linear complexity), and demonstrate its quality to query and cluster datasets containing large textile samples. As, to the best of our knowledge, this is the first practical approach for explicitly modelling complex and irregular weaving patterns usable for retrieval, we aim at establishing a solid baseline.

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“…Graph representation allows the relational knowledge of interacting entities to be stored and accessed efficiently [32]. e analysis of graph data can provide significant insights into community detection [33], behaviour analysis [34], and other applications (e.g., node classification [35], link prediction [36], clustering [37], and recommendations [38,39]). e descriptions and formats of graphs stored in a computer vary, and graph embedding is the most popular numeric-based graph structure description.…”

Section: Social Relationship Representations In Computer Sciencementioning

confidence: 99%

Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

Huang

Liu

2021

Discrete Dynamics in Nature and Society

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People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

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Heterogeneous graph-based joint representation learning for users and POIs in location-based social network

Cited by 57 publications

References 8 publications

A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

Structural textile pattern recognition and processing based on hypergraphs

Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

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