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Web Service Recommendation Based on Word Embedding and Node Embedding

Bo Jiang, Hang Li, Junchen Yang, Yanbin Qin, Liuhai Wang, Weifeng Pan, Hammad Afzal
2022 Mobile Information Systems  
In order to solve the deficiency of the collaborative filtering algorithm, we propose an improved hybrid method that combines the two kinds of information to generate word embedding and node embedding,  ...  However, this brings some difficulties for mashup (a kind of Web API composition) developers to choose appropriate Web Services to build their projects.  ...  However, too much information is easy to bring more interference to the approach.  ... 
doi:10.1155/2022/5106001 fatcat:x4zjqp35p5cqfc5o5ma6pvpxai

Robust Gram Embeddings

Taygun Kekec, David M. J. Tax
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
Word embedding models learn vectorial word representations that can be used in a variety of NLP applications.  ...  We propose a regularized embedding formulation, called Robust Gram (RG), which penalizes overfitting by suppressing the disparity between target and context embeddings.  ...  We also would like to thank Hamdi Dibeklioglu and Mustafa Unel for their kind support during this work.  ... 
doi:10.18653/v1/d16-1113 dblp:conf/emnlp/KekecT16 fatcat:nauo2v6g3zapdbvodwklnwjftq

Graph Embedding for Citation Recommendation [article]

Haofeng Jia, Erik Saule
2018 arXiv   pre-print
outperforms embedding based rankings for both neighborhood construction strategies.  ...  We also demonstrated that graph embedding is a robust approach for citation recommendation when hidden ratio changes, while the performance of classic methods drop significantly when the set of seed papers  ...  Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. 1652442.  ... 
arXiv:1812.03835v1 fatcat:y7rvizwftzhobcoe2xh5ecshta

MoNet: Moments Embedding Network [article]

Mengran Gou, Fei Xiong, Octavia Camps, Mario Sznaier
2018 arXiv   pre-print
Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple vision tasks.  ...  Additionally, recent results have shown that significant performance gains can be achieved by adding 1st order information and applying matrix normalization to regularize unstable higher order information  ...  We believe that this is due to the different approaches used to deal with rank deficiency.  ... 
arXiv:1802.07303v2 fatcat:okjkbgnzfjgonknxmcbqb4d26e

MoNet: Moments Embedding Network

Mengran Gou, Fei Xiong, Octavia Camps, Mario Sznaier
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple vision tasks.  ...  Additionally, recent results have shown that significant performance gains can be achieved by adding 1st order information and applying matrix normalization to regularize unstable higher order information  ...  We believe that this is due to the different approaches used to deal with rank deficiency.  ... 
doi:10.1109/cvpr.2018.00335 dblp:conf/cvpr/GouXCS18 fatcat:f6s7pn2wnzgq7kusgsdjchyram

Generalized Modularity Embedding: a General Framework for Network Embedding [article]

Cheng-Shang Chang, Ching-Chu Huang, Chia-Tai Chang, Duan-Shin Lee and Ping-En Lu
2019 arXiv   pre-print
The network embedding problem aims to map nodes that are similar to each other to vectors in a Euclidean space that are close to each other.  ...  Like centrality analysis (ranking) and community detection, network embedding is in general considered as an ill-posed problem, and its solution may depend on a person's view on this problem.  ...  The main reason is that random walk based approaches use random walks to enrich the neighbors of the nodes, but this may bring in noises due to the randomness of high degree nodes. VI.  ... 
arXiv:1904.11027v1 fatcat:5tfiv5j6zjalrblazkeks2hbxi

struc2gauss: Structural role preserving network embedding via Gaussian embedding

Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy
2020 Data mining and knowledge discovery  
Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors.  ...  In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information  ...  Gaussian embedding trains with a ranking-based loss based on the ranks of positive and negative samples.  ... 
doi:10.1007/s10618-020-00684-x fatcat:f5ad4xjetzaifargiq7rekifba

Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations (

Trevor Cohen, Dominic Widdows
2018 Proceedings of the 22nd Conference on Computational Natural Language Learning  
These findings demonstrate the importance of order-based information in analogical retrieval tasks, and the utility of random permutations as a means to augment neural embeddings.  ...  This paper presents a new method for incorporating word order information into word vector embedding models by combining the benefits of permutation-based order encoding with the more recent method of  ...  Order-based distributional models In most word vector embedding models based on sliding windows, the relative position of words within this sliding window is ignored, but there have been prior efforts  ... 
doi:10.18653/v1/k18-1045 dblp:conf/conll/CohenW18 fatcat:qvjqn7ryubeazfk4cxzj6btzr4

Measuring associational thinking through word embeddings

Carlos Periñán-Pascual
2021 Artificial Intelligence Review  
We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these  ...  Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that  ...  However, the rank-ordering problem needs a relative function with respect to the remaining elements. Otherwise, a strong bias towards top-ranked elements can be introduced.  ... 
doi:10.1007/s10462-021-10056-6 fatcat:oga5x5voubglvokozk5o7vjlke

Embedding-aided network dismantling [article]

Saeed Osat, Fragkiskos Papadopoulos, Andreia Sofia Teixeira, Filippo Radicchi
2022 arXiv   pre-print
Once a network is embedded, dismantling is implemented using intuitive geometric strategies. We demonstrate that the approach well suits both Euclidean and hyperbolic network embeddings.  ...  In this paper, we show that network representations in geometric space can be used to solve several variants of the network dismantling problem in a coherent fashion.  ...  For site percolation, we rank nodes in descending order based on the value of their degree centrality, with eventual ties randomly broken.  ... 
arXiv:2208.01087v1 fatcat:wmzp5a6s6jgijnbhyx4pyaoiky

Combining Text Embedding and Knowledge Graph Embedding Techniques for Academic Search Engines

Gengchen Mai, Krzysztof Janowicz, Bo Yan
2018 International Semantic Web Conference  
Next, the semantic similarity between papers and entities can be measured based on the learned embedding models.  ...  Paragraph vector and knowledge graph embedding are used to embed papers and entities into low dimensional hidden space.  ...  In order to analyze the dynamics of diachronic topic-based research communities, a hybrid semantic approach has been developed by Osborne et al [12] .  ... 
dblp:conf/semweb/MaiJY18 fatcat:o2ehtvu7zjb4ljjraorwnxcnlq

Virtual network embedding on massive substrate networks

Chenggui Zhao, Behrooz Parhami
2020 Transactions on Emerging Telecommunications Technologies  
To alleviate the computational burden of previous virtual network embedding (VNE) approaches when the resource network scales up significantly, we propose an efficient node ranking strategy that considers  ...  Finally, the subgraph nodes are ranked according to a local node ranking vector derived from a random-walking scheme.  ...  based on node ranking.  ... 
doi:10.1002/ett.3849 fatcat:6kr7fihzgvcdpnbhlt7lqpw5zm

Name Disambiguation in Anonymized Graphs using Network Embedding [article]

Baichuan Zhang, Mohammad Al Hasan
2017 arXiv   pre-print
To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique  ...  In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical  ...  By assuming all the ranking orders generated from the linked document network G dd to be independent, the probability P(> |D) of all the ranking orders being preserved given the document embedding matrix  ... 
arXiv:1702.02287v4 fatcat:wzzuhqlrvbaine5ajd3uhm2c5q

Dynamic Embeddings for Interaction Prediction [article]

Zekarias T. Kefato and Sarunas Girdzijauskas and Nasrullah Sheikh and Alberto Montresor
2021 arXiv   pre-print
Experiments show that DeePRed outperforms the best state-of-the-art approach by at least 14% on next item prediction task, while gaining more than an order of magnitude speedup over the best performing  ...  Although this study is mainly concerned with temporal interaction networks, we also show the power and flexibility of DeePRed by adapting it to the case of static interaction networks, substituting the  ...  Moreover, these approaches work by recursively computing the short-term embedding at time based on the embedding at time − 1 , which leads to sequential training that proved to be a bottleneck as the network  ... 
arXiv:2011.05208v2 fatcat:dlbsmewqhfgt5ce2fhntotf7fq

SepNE: Bringing Separability to Network Embedding [article]

Ziyao Li and Liang Zhang and Guojie Song
2019 arXiv   pre-print
We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes.  ...  for entire networks even when only a small proportion of nodes are of interest.  ...  SepNE: Separated Network Embedding In this section, we propose SepNE, a simple and separable NE approach based on SMF. A general framework of SepNE is presented in Algorithm 1.  ... 
arXiv:1811.05614v2 fatcat:nbdw6ve6e5cynlh7qsvmjipewm
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