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Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings
2017
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
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In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically ...
Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. ...
Based on that a parameter free hierarchical graph-based clustering approach is developed that is the basis of a tool that allows to explore the neighborhood of a term of interest. ...
doi:10.18653/v1/w17-2404
dblp:conf/textgraphs/TrostK17
fatcat:zkau2afd6fdstigzibjdvbzpmu
Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection
[article]
2019
arXiv
pre-print
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To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. ...
These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. ...
based on parameter-free optimization of generalized modularity to produce effective embeddings. ...
arXiv:1912.08808v1
fatcat:e5yhpvy2hfej7kxndykmit5xte
Semantic Unsupervised Automatic Keyphrases Extraction by Integrating Word Embedding with Clustering Methods
2020
Multimodal Technologies and Interaction
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The main feature of this approach is the integration of two methods that have given interesting results: word embedding models, such as Word2Vec or GloVe able to capture the semantics of words and their ...
context, and clustering algorithms, able to identify the essence of the terms and choose the more significant one(s), to represent the contents of a text. ...
Keyphrase extraction methods in unsupervised approaches can be grouped into statistical-based, graph-based, and cluster-based approaches. ...
doi:10.3390/mti4020030
fatcat:7gyy4jtev5frxgh577ksesea6i
Persona2vec: a flexible multi-role representations learning framework for graphs
2021
PeerJ Computer Science
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Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. ...
Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. ...
ACKNOWLEDGEMENTS For their comments, we thank Sadamori Kojaku, Alessandro Flammini, Filippo Menczer, Xiaoran Yan, Filipi Nascimento Silva, and Minwoo Ahn. ...
doi:10.7717/peerj-cs.439
pmid:33834106
pmcid:PMC8022511
fatcat:fmcqkcxhbzau7grtjjoispryya
Community Detection Based on DeepWalk Model in Large-Scale Networks
2020
Security and Communication Networks
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In order to detect community structure in large-scale networks more accurately and efficiently, we propose a community detection algorithm based on the network embedding representation method. ...
By further analyzing the excavated community structure, the organizational characteristics within the community are better revealed. ...
A family of densities is used over the latent variables, parameterized by free variational parameters. e optimization finds the member of this family, i.e., the setting of the parameters, which is the ...
doi:10.1155/2020/8845942
doaj:adb9f4e603aa4c4397a6e6e45434d8b9
fatcat:kngocha7lbe2dlutzz2ri5drs4
HUMAP: Hierarchical Uniform Manifold Approximation and Projection
[article]
2021
arXiv
pre-print
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for most data types. ...
For datasets containing many granularities or when analysis follows the information visualization mantra, hierarchical DR techniques are the most suitable approach since they present major structures beforehand ...
For example, HUMAP shows the continuous nature of the EB dataset even for top-level embedding. ...
arXiv:2106.07718v2
fatcat:za5wg2bym5b2rkjq2bh3muugfa
Hierarchical lifelong topic modeling using rules extracted from network communities
2022
PLoS ONE
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The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. ...
To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). ...
In particular, rules are extracted by constructing a graph based on the data in textual documents and then communities of related words are detected within the graph using spectral clustering. ...
doi:10.1371/journal.pone.0264481
pmid:35239700
pmcid:PMC8893656
fatcat:gpwnrciaoza4powmjrwawfpsa4
Fluid Flow Complexity in Fracture Networks: Analysis with Graph Theory and LBM
[article]
2012
arXiv
pre-print
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Also, for each type of fluid regime, corresponding motifs shapes are scaled. ...
Through this research, embedded synthetic fracture networks in rock masses are studied. ...
In other words, our fracture algorithm (with or without modularity), for a wide range of parameter variations, gives "scale-free networks". ...
arXiv:1107.4918v2
fatcat:dvgcpxz7gbborifmtfzsk7epny
Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph Pooling Fusion
[article]
2021
arXiv
pre-print
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For the first stage, a graph convolutional network is employed for each modality to learn intra-modal dynamics. ...
Specifically, our Multimodal Graph is hierarchically structured to cater to two stages, i.e., intra- and inter-modal dynamics learning. ...
As for graph pooling, the DiffPool [50] learns a differentiable soft cluster strategy for nodes using node embedding, mapping nodes to a set of clusters. ...
arXiv:2011.13572v3
fatcat:dyjfr5vidnheliuupsntv255da
Network representation learning: models, methods and applications
2019
SN Applied Sciences
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We provide a taxonomy of node embedding methods based on the type of the networks. ...
A few efforts are already made to survey [22, 38, 46 , 89] the various approaches for network embedding. ...
Acknowledgements The authors would like to thank the management and staff of Department of Computer Applications, CUSAT, India and NSS College of Engineering, Palakkad, India for providing enough materials ...
doi:10.1007/s42452-019-1044-9
fatcat:zvlbj4qozzfw3dxoyevb6wgska
Citation Intent Classification Using Word Embedding
2021
IEEE Access
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We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. ...
It was found that BERT embedding performs significantly better, having an 89% Precision score. ...
We created clusters of the sentences based on their contextual meanings. We then analyzed each of the clusters and tagged them for citation intent. ...
doi:10.1109/access.2021.3050547
fatcat:z4srkzffkvgf5leanmn3gp5u6m
Patient Similarity via Joint Embeddings of Medical Knowledge Graph and Medical Entity Descriptions
2020
IEEE Access
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Specifically, DeepPS incorporates medical entity descriptions by augmenting the embeddings of medical entities and relations with the embeddings of words, which leverages both information from medical ...
Existing studies model EHRs by medical knowledge graph embedding to learn the latent embeddings of medical entities (e.g., patients, medications, diagnoses and procedures). ...
To analyze the influence of the parameters in the similarity learning performance of the proposed framework, we perform a parameter sensitivity evaluation for the four key parameters: the margin b, the ...
doi:10.1109/access.2020.3019577
fatcat:oi2mlecurzh4pcm4pnhnssraxa
Network Representation Learning: From Traditional Feature Learning to Deep Learning
2020
IEEE Access
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after word embeddings. ...
Because nodes after embedding process are real-valued vectors, density-based clustering is able to leverage the vector to perform node clustering tasks in graphs [134] , [135] . ...
doi:10.1109/access.2020.3037118
fatcat:kca6htfarjdjpmtwcvbsppfzui
FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques
2020
Frontiers in Big Data
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The resulting model obtained after training on AGROVOC was evaluated against the state-of-the-art word embedding and knowledge graph embedding models that were trained on the same dataset. ...
terms for describing FEW concepts. ...
ACKNOWLEDGMENTS We are thankful to the reviewers for their excellent comments and suggestions. This work was supported in part by the National Science Foundation under grant No. 1747751. ...
doi:10.3389/fdata.2020.00012
pmid:33693387
pmcid:PMC7931944
fatcat:3vaonvpuabhinhgdn5ehmvvt5u
Multi-document Summarization via Deep Learning Techniques: A Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2011.04843v2 fatcat:lwdpyj64m5ei7aagyfcu24a5z4Other Versions
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. ...
Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models. ...
For the word/sentence-level concatenation methods, clustering algorithms and graph-based techniques are the most commonly used methods. More specifically, Mir et al. ...
arXiv:2011.04843v3
fatcat:zfi52xxef5g2tjkaw6hgjpwa5i
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