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Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique
[chapter]
2012
Lecture Notes in Computer Science
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Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. ...
In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named "fuzzy multilevel graph embedding -FMGE", through feature selection technique ...
Feature Selection by Ranking Discriminatory Features The feature vector obtained by FMGE is based on histogram encoding of the multilevel information extracted from graph. ...
doi:10.1007/978-3-642-34166-3_27
fatcat:zxl3fcf775c2hm23vurc3yfqd4
Little Ball of Fur: A Python Library for Graph Sampling
[article]
2020
arXiv
pre-print
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Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features. ...
We show the practical usability of the library by estimating various global statistics of social networks and web graphs. ...
Using these embeddings as input features for node and graph classification tasks we establish that the embeddings learned on the subsampled graphs extract high quality features. Our contributions. ...
arXiv:2006.04311v2
fatcat:acolhpjfefbvpgwcuklm3c2zna
Little Ball of Fur
2020
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features. ...
We show the practical usability of the library by estimating various global statistics of social networks and web graphs. ...
Using these embeddings as input features for node and graph classification tasks we establish that the embeddings learned on the subsampled graphs extract high quality features. Our contributions. ...
doi:10.1145/3340531.3412758
dblp:conf/cikm/RozemberczkiKS20a
fatcat:sqxrjqzyxzbufanmg5fyp6cxpu
A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition
[chapter]
2013
Lecture Notes in Computer Science
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Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of ...
In recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computational expensive graph based representations to benefit from mature, less ...
Method 3: Attribute Statistics based Embedding The attribute statistics based embedding of graphs is a simple and efficient way of expressing the labelling information stored in nodes and edges of graphs ...
doi:10.1007/978-3-642-38221-5_9
fatcat:pzp6r7hjgjbwrmbykpjapbz6t4
A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding
2021
Computational Intelligence and Neuroscience
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To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector ...
of entity relationship and the embedding vector of subgraph structure feature. ...
Entity Disambiguation Based on Graph Neural Network. e final entity disambiguation is based on the similarity measurement of the entity embedding vector V KGE and the entity structure embedding vector ...
doi:10.1155/2021/2878189
pmid:34603428
pmcid:PMC8486511
fatcat:yyh34x6zfbhgdntxzurlpgwdki
Using ontology embeddings for structural inductive bias in gene expression data analysis
[article]
2020
arXiv
pre-print
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Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. ...
We use ontology embeddings that capture the semantic similarities between the genes to direct a Graph Convolutional Network, and therefore sparsify the network connections. ...
Moreover, the ontology-based feature selection allows selecting a biologically relevant set of features. ...
arXiv:2011.10998v1
fatcat:px2whb62q5apjesojodgjq7hsu
Explainability-based Backdoor Attacks Against Graph Neural Networks
[article]
2021
arXiv
pre-print
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For instance, on the node classification task, the backdoor attack with trigger injecting position selected by GraphLIME reaches over 84 % attack success rate with less than 2.5 % accuracy drop ...
There are already numerous works on backdoor attacks on neural networks, but only a few works consider graph neural networks (GNNs). ...
node classification task based on different trigger features selecting strategies. ...
arXiv:2104.03674v2
fatcat:iip3rirgtbbtpljsk4d24vdanu
Comparative effectiveness of medical concept embedding for feature engineering in phenotyping
2021
JAMIA Open
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Of MCEs based on knowledge graphs and EHR data, MCEs learned by using node2vec with knowledge graphs and MCEs learned by using GloVe with EHR data outperforms other MCEs, respectively. ...
Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. ...
of the nodes in a graph using random walk. ...
doi:10.1093/jamiaopen/ooab028
pmid:34142015
pmcid:PMC8206403
fatcat:tmxwmx7cnfcgbgvzmldpdczkqm
Graph-based Neural Multi-Document Summarization
[article]
2017
arXiv
pre-print
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We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. ...
In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural ...
Acknowledgements We would like to thank Mirella Lapata, the members of the Sapphire Project (University of Michigan and IBM), as well as all the anonymous reviewers for their helpful suggestions on this ...
arXiv:1706.06681v3
fatcat:nq6s6fmdpzetnhtqbws7c6depm
Graph embedding in vector spaces by node attribute statistics
2012
Pattern Recognition
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The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. ...
statistical learning machinery to be used on graph-based input patterns. ...
Kaspar Riesen for providing us with the databases that are used in this work and the corresponding implementations of the reference systems. ...
doi:10.1016/j.patcog.2012.01.009
fatcat:ubz2fdx7p5hmjp56luihghyeqa
Graph Embedding Using Constant Shift Embedding
[chapter]
2010
Lecture Notes in Computer Science
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In this paper, we propose a graph embedding technique based on the constant shift embedding which transforms a graph to a real vector. ...
This technique gives the abilities to perform the graph classification tasks by procedures based on feature vectors. ...
This method was originally developed for the embedding of feature vectors in a dissimilarity space [16, 17] and is based on the selection of some prototypes and the computation of the graph edit distance ...
doi:10.1007/978-3-642-17711-8_9
fatcat:xwjktu425nhadm6tptbcmxq4aa
Graph-based Neural Multi-Document Summarization
2017
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
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We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. ...
In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural ...
Acknowledgements We would like to thank the members of the Sapphire Project (University of Michigan and IBM), as well as all the anonymous reviewers for their helpful suggestions on this work. ...
doi:10.18653/v1/k17-1045
dblp:conf/conll/YasunagaZMPSR17
fatcat:qshfkz4torefjhuzagpsi2rlaq
Hypergraph Partitioning With Embeddings
[article]
2020
arXiv
pre-print
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In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. ...
A hypergraph is a generalization of a traditional graph wherein "hyperedges" may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. ...
We would like to thank Sebastian Schlag from the Karlsruhe Institute of Technology for helping us to understand KaHyPar. We would also like to thank our editor and reviewers. ...
arXiv:1909.04016v5
fatcat:wfpycc6sirdsnasocgyp72375a
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
[article]
2019
arXiv
pre-print
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2020 accepted arXiv:1906.05017v3 fatcat:zxmqjhafhjgd3hyzqk4bzblbaqOther Versions
the more recent graph embedding methods (e.g., random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. ...
Besides, compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features ...
Petrone, Kaushik Mani and anonymous reviewers for their helpful comments and suggestions on our work, and Ohio Supercomputer Center (OSC) (Ohio Supercomputer Center, 1987) for providing us computing resources ...
arXiv:1906.05017v2
fatcat:7k6vrdkwybdu7pikmxa3xrnowm
On the Correlation of Graph Edit Distance and L 1 Distance in the Attribute Statistics Embedding Space
[chapter]
2012
Lecture Notes in Computer Science
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In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported ...
We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features ...
In other words, this kind of embedding is based on occurrence and co-occurrence statistics of labels in the underlying graph. ...
doi:10.1007/978-3-642-34166-3_15
fatcat:cwinatggwzdldoex5c3m5ttike
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