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Predicting Neighbor Distribution in Heterogeneous Information Networks [article]

Yuchi Ma and Ning Yang and Chuan Li and Lei Zhang and Philip S. Yu
2015 arXiv   pre-print
applications in heterogeneous information networks.  ...  In this paper, we present a new prediction task, Neighbor Distribution Prediction (NDP), which aims at predicting the distribution of the labels on neighbors of a given node and is valuable for many different  ...  Conclusion In this paper, we present a new prediction problem, Neighbor Distribution Prediction in heterogeneous information network.  ... 
arXiv:1506.01760v1 fatcat:v2neq7p7dfc3zbldyeuyzpekua

Predicting Neighbor Distribution in Heterogeneous Information Networks [chapter]

Yuchi Ma, Ning Yang, Chuan Li, Lei Zhang, Philip S. Yu
2015 Proceedings of the 2015 SIAM International Conference on Data Mining  
applications in heterogeneous information networks.  ...  In this paper, we present a new prediction task, Neighbor Distribution Prediction (NDP), which aims at predicting the distribution of the labels on neighbors of a given node and is valuable for many different  ...  Conclusion In this paper, we present a new prediction problem, Neighbor Distribution Prediction in heterogeneous information network.  ... 
doi:10.1137/1.9781611974010.88 dblp:conf/sdm/MaYLZY15 fatcat:s3gus4ppnzcc7fxqccjvbvpz7e

Machine learning dynamical phase transitions in complex networks [article]

Qi Ni, Ming Tang, Ying Liu, Ying-Cheng Lai
2019 arXiv   pre-print
of complex dynamical systems in general.  ...  with their neighbors and another based on k-core of the network.  ...  In an actual situation, we may not know all the label information of a training set, especially when the state of the underlying dynamical network is near the threshold.  ... 
arXiv:1911.04633v1 fatcat:iilz4qbr3bc7jh44meofcswvru

Multi-relational Link Prediction in Heterogeneous Information Networks

Darcy Davis, Ryan Lichtenwalter, Nitesh V. Chawla
2011 2011 International Conference on Advances in Social Networks Analysis and Mining  
Finally, we present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting  ...  In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.  ... 
doi:10.1109/asonam.2011.107 dblp:conf/asunam/DavisLC11 fatcat:vcvyjqrzgjg3jkdyxzea6a67ka

Network representation learning: models, methods and applications

Anuraj Mohan, K. V. Pramod
2019 SN Applied Sciences  
Networks are classified into broader categories such as homogeneous networks, heterogeneous networks, attributed networks, signed networks, and dynamic networks.  ...  Definition 4 A heterogeneous network is a network G = (V , E) , where each node v i ∈ V and each edge e i ∈ E , are associated with mapping functions F(v) ∶ V → T v and f (e) ∶ E → T e , where T v and  ...  Link prediction is an important challenge in dynamic networks and the significance of using node representations for link prediction in dynamic networks was tested by TNE using various network datasets  ... 
doi:10.1007/s42452-019-1044-9 fatcat:zvlbj4qozzfw3dxoyevb6wgska

Supervised methods for multi-relational link prediction

Darcy Davis, Ryan Lichtenwalter, Nitesh V. Chawla
2012 Social Network Analysis and Mining  
We present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting.  ...  In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.  ... 
doi:10.1007/s13278-012-0068-6 fatcat:mvqo6so5rnbbhcj6nsyq6humfu

Ubiquitous Data-Centric Sensor Networks

Ting Yang, Peng-Yung Woo, Zhaoxia Wang, Javid Taheri, Chin Hoong Choor, Guoqiang Hu
2014 International Journal of Distributed Sensor Networks  
The distributed agents QoS routing algorithm in the paper "A reliable transport protocol with International Journal of Distributed Sensor Networks 3 prediction mechanism for urgent information in wireless  ...  The paper "A reliable transport protocol with prediction mechanism for urgent information in wireless sensor networks" addressed a reliable transport protocol with prediction mechanism for urgent information  ... 
doi:10.1155/2014/459768 fatcat:dwqtffztobb65gkmsho2jkzkei

Modeling complex networks for electronic commerce

Foster Provost, Arun Sundararajan
2007 Proceedings of the 8th ACM conference on Electronic commerce - EC '07  
Shows that the optimal way to "seed" a network can often involve targeting the least connected nodes in addition to the most connected ones (and sometimes excluding the most connected ones).  ...  Establish existence of an extension of the Arrow-Debreu equilibrium in which "local" markets clear.  ...  (1997 The problem: Prediction in Networked Data 29 The problem: Prediction in Networked Data ?  ... 
doi:10.1145/1250910.1250970 dblp:conf/sigecom/ProvostS07 fatcat:kutk525dc5a43gecr6qdjjthry

Scalable Label Propagation Algorithms for Heterogeneous Networks [article]

Erfan Farhangi Maleki, Nasser Ghadiri, Maryam Lotfi Shahreza, Zeinab Maleki
2018 arXiv   pre-print
In this paper, two distributed label propagation algorithms, namely DHLP-1 and DHLP-2, in the heterogeneous networks have been introduced.  ...  Complex heterogeneous networks have many examples in the real world and are widely used today for modeling complicated processes. Biological networks are one of such networks.  ...  The proposed label propagation algorithms We propose two distributed heterogeneous label propagation algorithms to predict different types of potential interactions in the network efficiently and accurately  ... 
arXiv:1811.09018v1 fatcat:hcr2ksuqw5aufpzdbumlgplkru

Fraud Detection in Online Product Review Systems via Heterogeneous Graph Transformer

Songkai Tang, Luhua Jin, Fan Cheng
2021 IEEE Access  
The detector f is trained based on the labeled node information in a semi-supervised manner. For example, the node could be an account in a transaction system or a user in a social network.  ...  [24] firstly build a model learning structure information among reviewers, reviews, and stores while NetWalk [25] extends fraud detection to dynamic networks.  ... 
doi:10.1109/access.2021.3084924 fatcat:wzzwnmdptnfm5hvarripls7heu

Link Prediction in Social Networks: the State-of-the-Art [article]

Peng Wang and Baowen Xu and Yurong Wu and Xiaoyu Zhou
2014 arXiv   pre-print
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks.  ...  In the past decade, many works have been done about the link prediction in social networks.  ...  To predict the opinion holder in such heterogeneous social network without labeled data, Kuo et al. generalized it to a link prediction with aggregative statistics problem and proposed an unsupervised  ... 
arXiv:1411.5118v2 fatcat:ns5ufekku5hwnotjlfgj2oiaei

A network criterion for the success of cooperation in an evolutionary prisoner's dilemma, and a variation on Hamilton's rule [article]

Stephen Devlin, Thomas Treloar
2009 arXiv   pre-print
We show that the success of cooperation in an evolutionary prisoner's dilemma on a complex network can be predicted by a simple, quantitative network analysis using mean field parameters.  ...  The criterion is shown to be accurate on a wide variety of networks with degree distributions ranging from regular to Poisson to scale-free.  ...  The degree distribution ignores any other contact information present in the network, so G(x) represents a generic network chosen randomly from among all those with the fixed degree distribution.  ... 
arXiv:0908.3834v1 fatcat:gjh7gkoh45dxloqjiboa2gktme

Link prediction in social networks: the state-of-the-art

Peng Wang, BaoWen Xu, YuRong Wu, XiaoYu Zhou
2014 Science China Information Sciences  
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks.  ...  In the past decade, many works have been done about the link prediction in social networks.  ...  To predict the opinion holder in such heterogeneous social network without labeled data, Kuo et al.  ... 
doi:10.1007/s11432-014-5237-y fatcat:x6jd4zwg7fgefjrho4en4rtfgm

AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting [article]

Qinyi Zhu, Yiou Xiao
2021 arXiv   pre-print
As a special field in deep learning, Graph Neural Networks (GNNs) focus on extracting intrinsic network features and have drawn unprecedented popularity in both academia and industry.  ...  Most recent GNNs follow an encoder-decoder paradigm to encode high dimensional heterogeneous information from a subgraph onto one low dimensional embedding space.  ...  between two node embeddings; b) Node Recommendation, where the node-level labels are predicted only using the information of neighbors excluding themselves; c) Multi-Task Learning, where link prediction  ... 
arXiv:2108.06452v1 fatcat:ggtcjkaehvgabpmtsnmpp746iu

Misinformation spreading on correlated multiplex networks [article]

Jiajun Xian, Dan Yang, Liming Pan, Wei Wang, Zhen Wang
2019 arXiv   pre-print
Our proposed model and accurate theoretical analysis will serve as a useful framework to understand and predict the spreading dynamics of misinformation on multiplex networks, and thereby pave the way  ...  Subsequently, we develop a heterogeneous edge-base compartmental theory to comprehend the spreading dynamics of our proposed model.  ...  Secondly, we pay attention to the influence of the degree heterogeneity on the spreading dynamics, as most real-world networks display heterogeneous degree distribution.  ... 
arXiv:1909.00397v1 fatcat:ea6dtiujbvbcbci7u3awgfrtwa
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