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Solving the missing node problem using structure and attribute information

Sigal Sina, Avi Rosenfeld, Sarit Kraus
2013 Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13  
However, previous works did not consider the possibility that information about specific users (nodes) within the network could be useful in solving this problem.  ...  Recently, the Missing Node Identification problem was introduced where missing members in the social network structure must be identified.  ...  ACKNOWLEDGMENT This research is based on work supported in part by MAFAT and the Google Interuniversity center for Electronic Markets and Auctions.  ... 
doi:10.1145/2492517.2492534 dblp:conf/asunam/SinaRK13 fatcat:qhegk4ynjnabjps5itmaheiexe

DEFINE: Friendship Detection Based on Node Enhancement [chapter]

Hanxiao Pan, Teng Guo, Hayat Dino Bedru, Qing Qing, Dongyu Zhang, Feng Xia
2020 Lecture Notes in Computer Science  
To improve the embedding performance, we consider both network structures and node attributes to learn node representations.  ...  Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework.  ...  Fig. 2 shows that the embedding part has network structures view T and node attributes view P , which uses deep autoencoder to learn latent information in each view.  ... 
doi:10.1007/978-3-030-39469-1_7 fatcat:pzflvx2kdzgrvdhnz2zxmuhm6y

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Minghu Tang, Wei Yu, Xiaoming Li, Xue Chen, Wenjun Wang, Zhen Liu
2022 Computer systems science and engineering  
Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other.  ...  Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction.  ...  Data Availability: The networks used in this study are available from http://snap.stanford.edu/data/, http:// vladowiki.fmf.uni-lj.si/doku.php?id=pajek:data:urls:index.  ... 
doi:10.32604/csse.2022.028841 fatcat:ovcxuvpsvvewddwpu4crrfzxnm

From Structure-Based to Semantics-Based: Towards Effective XML Keyword Search [chapter]

Thuy Ngoc Le, Huayu Wu, Tok Wang Ling, Luochen Li, Jiaheng Lu
2013 Lecture Notes in Computer Science  
Particularly, we propose to use Object Relationship (OR) graph, which fully captures semantics of object, relationship and attribute, to represent XML document and we develop algorithms based on the OR  ...  Experimental results show that our proposed semantics-based approach can resolve the problems of the structure-based search, and significantly improve both the effectiveness and efficiency.  ...  Problems can be solved with IDREF IDREF mechanism is based on semantics of object and object ID, thus using IDREF can avoid problems caused by lack of semantics of object, including the problems of missing  ... 
doi:10.1007/978-3-642-41924-9_29 fatcat:gkhyyqcckjdarps53wp5q7vfo4

SINE: Scalable Incomplete Network Embedding [article]

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
2018 arXiv   pre-print
Different from existing attributed network embedding algorithms, SINE provides greater flexibility to make the best of useful information and mitigate negative effects of missing information on representation  ...  Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations  ...  ACKNOWLEDGMENTS The work was supported by the US National Science Foundation (NSF) through grant IIS-1763452, and the Australian Research Council (ARC) through grant LP160100630 and DP180100966.  ... 
arXiv:1810.06768v1 fatcat:ghoaty6nq5eclgjsn5bk62bbqa

Data Mining In Oral Medicine Using Decision Trees

Fahad Shahbaz Khan, Rao Muhammad Anwer, Olof Torgersson, Göran Falkman
2008 Zenodo  
Data mining has been used very frequently to extract hidden information from large databases.  ...  In this way the extracted data could be used to teach students of oral medicine a number of orderly processes for dealing with patients who represent with different problems within the practice context  ...  The problem with step-wise exercises is that the students learn with one predefined structured thinking process for solving one type of problem.  ... 
doi:10.5281/zenodo.1335258 fatcat:4nqx62ia4bcknm5mun3rswgnrm

Conflict resolution and missing completion in the fusion of domain ontology in cyber-physical systems

Xiangfei Yan, Liwei Zheng, J. Zhu
2022 MATEC Web of Conferences  
Therefore, this paper provides a method for the conflict resolution and missing completion in the fusion of domain.  ...  CPS integrates information services, human resource services, and physical equipment services, and always be supported by the ontologies in multiple domains.  ...  Similarly, the above hard constraints and conclusion are used here, that is, the company is − of 3 P node edge should point to Ali node. Give the process of repairing the missing problem: • 1.  ... 
doi:10.1051/matecconf/202235503012 fatcat:tukkdpvwszekln6q7kbkvusp2u

LIM-G: Learner-initiating instruction model based on cognitive knowledge for geometry word problem comprehension

Wing-Kwong Wong, Sheng-Cheng Hsu, Shih-Hung Wu, Cheng-Wei Lee, Wen-Lian Hsu
2007 Computers & Education  
Using cognitive knowledge and frame-template structures, the system can extract the relevant concepts in geometry word problems for comprehension.  ...  Based on a learner-initiating instruction strategy, LIM-G first comprehends the problem and then gives the learner the telegraphic and diagrammatic representations of the problem, which are more 0360-1315  ...  Acknowledgements This research is supported by the National Science Council in Taiwan.  ... 
doi:10.1016/j.compedu.2005.03.009 fatcat:gfn2p2do5ralbmb2lboannn4uy

Reconstruction of Missing Big Sensor Data [article]

Yongshuai Shao, Zhe Chen
2017 arXiv   pre-print
Finally, we evaluate the approaches using two real sensor datasets with two missing data-patterns, i.e., random missing pattern and consecutive missing pattern.  ...  One of the important sources for scientific big data is the datasets collected by Internet of things (IoT). It's considered that these datesets contain highly useful and valuable information.  ...  ACKNOWLEDGMENT This work is supported by the Fundamental Research Funds for the Central Universities (N140404015).  ... 
arXiv:1705.01402v1 fatcat:n6mleloqvvb6nhrtswuvepxjfy

Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN) [article]

Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song
2012 arXiv   pre-print
The effects of social influence and homophily suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference.  ...  Recently, Yin et al. proposed Social-Attribute Network (SAN), an attribute-augmented social network, to integrate network structure and node attributes to perform both link prediction and attribute inference  ...  In this work, we simultaneously use network structure and node attribute information to improve performance of both the link prediction and the attribute inference problems, motivated by the observed interaction  ... 
arXiv:1112.3265v9 fatcat:fh2lf5njtveapf7bzim5pky6ke

Graph Augmentation Learning [article]

Shuo Yu, Huafei Huang, Minh N. Dao, Feng Xia
2022 arXiv   pre-print
Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels.  ...  We further detailedly illustrate how GAL enhance the data quality and the model performance.  ...  HGNN-AC Jin et al. [2021a] use graph attention mechanism to complete the missing attributes of nodes in heterogeneous graphs, avoiding using previous hand-crafted ways to solve this problem.  ... 
arXiv:2203.09020v1 fatcat:72esohvrxbdzdlnahaa27pftqm

Learning on Attribute-Missing Graphs

Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya Zhang, Ivor W. Tsang
2020 IEEE Transactions on Pattern Analysis and Machine Intelligence  
This attribute-missing graph is related to numerous real-world applications and there are limited studies investigating the corresponding learning problems.  ...  SAT leverages structures and attributes in a decoupled scheme and achieves the joint distribution modeling of structures and attributes by distribution matching techniques.  ...  In our problem, only graph structures are used as input and encoded as latent codes. Then the latent codes are decoded as node attributes.  ... 
doi:10.1109/tpami.2020.3032189 pmid:33074805 fatcat:yx3qnk7gfzhxhowpvhcoexfxyi

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine (Runting) Shi, Dawn Song
2014 ACM Transactions on Intelligent Systems and Technology  
The effects of social influence and homophily suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference.  ...  Moreover, we make the novel observation that attribute inference can help inform link prediction, i.e., link prediction accuracy is further improved by first inferring missing attributes.  ...  In this work, we simultaneously use network structure and node attribute information to improve performance of both the link prediction and the attribute inference problems, motivated by the observed interaction  ... 
doi:10.1145/2594455 fatcat:3kvor2tzszegvdhnmoiwnyqaga

Anchor Link Prediction across Attributed Networks via Network Embedding

Shaokai Wang, Xutao Li, Yunming Ye, Shanshan Feng, Raymond Lau, Xiaohui Huang, Xiaolin Du
2019 Entropy  
structure and attributes as input.  ...  Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem.  ...  Despite great application values, solving the problem is challenging, because of the complex network structures, the rich attribute information, and few observed anchor links.  ... 
doi:10.3390/e21030254 pmid:33266969 pmcid:PMC7514735 fatcat:sfvuzfcherdnbilirbavcmcisq

Latent Attribute Inference of Users in Social Media with Very Small Labeled Dataset

Ding XIAO, Rui WANG, Lingling WU
2016 IEICE transactions on information and systems  
Then SRW-COND employs a supervised random walk process to effectively utilize the known attributes information and link structure of users.  ...  In this paper, we study the latent attribute inference problem with very small labeled data and propose the SRW-COND solution.  ...  Link prediction problem was solved in [15] , while our work aims to solve the attribute inference problem.  ... 
doi:10.1587/transinf.2016edp7049 fatcat:yrpepf6hmfhapjkkorr527xaba
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