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Link prediction

Rong-Hua Li, Jeffrey Xu Yu, Jianquan Liu
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
To address this issue, we use maximal entropy random walk (MERW) for link prediction, which incorporates the centrality of nodes of the network.  ...  Finally, to exhibit the power of MERW in link prediction, we compare 27 various link prediction methods over 3 synthetic and 8 real networks.  ...  Acknowledgments The work was supported by grants of the Research Grants Council of the Hong Kong SAR, China No. 419008 and 419109.  ... 
doi:10.1145/2063576.2063741 dblp:conf/cikm/LiYL11 fatcat:s3l4eak5drehhepmh5uefjlvti

Pairwise Learning for Neural Link Prediction [article]

Zhitao Wang, Yong Zhou, Litao Hong, Yuanhang Zou, Hanjing Su, Shouzhi Chen
2022 arXiv   pre-print
The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function.  ...  We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2.  ...  Acknowledgments The authors greatly thank the great support for advanced research from departments of WeChat Pay and WeChat Search.  ... 
arXiv:2112.02936v6 fatcat:q3rzbj33h5aohgncppbjrl3vda

Link prediction based on path entropy

Zhongqi Xu, Cunlai Pu, Jian Yang
2016 Physica A: Statistical Mechanics and its Applications  
In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks.  ...  Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths.  ...  Entropy of network dynamics such as diffusion process [56] and random walks [57] are also discussed.  ... 
doi:10.1016/j.physa.2016.03.091 fatcat:yntygtvhybbz5b7dhizka6uuhi

Link Prediction Adversarial Attack [article]

Jinyin Chen, Ziqiang Shi, Yangyang Wu, Xuanheng Xu, Haibin Zheng
2018 arXiv   pre-print
With the wider application of deep model in complex network analysis, in this paper we define and formulate the link prediction adversarial attack problem and put forward a novel iterative gradient attack  ...  We can benefit the attack as an efficient privacy protection tool from link prediction unknown violation, on the other hand, link prediction attack can be a robustness evaluation metric for current link  ...  ACKNOWLEDGMENTS This work is partially supported by National Natural Science Foundation of China (61502423, 61572439), Zhejiang Science and Technology Plan Project (LGF18F030009), Zhejiang University of  ... 
arXiv:1810.01110v2 fatcat:3oe3naulcfe25nii4ehfpj2j3u

Link Prediction Based on the Derivation of Mapping Entropy

Hefei Hu, Yanan Wang, Zheng Li, Yang Tian, Yuemei Ren, Fei Xiong
2021 Complexity  
The algorithms based on topological similarity play an important role in link prediction.  ...  Through generous explorations, we propose the DME (derivation of mapping entropy) model concerning the mapping relationship between the node and its neighbors to access the influence of the node appropriately  ...  Superposed random walk (SRW) index [15] is one of the indices based on the Markov model.  ... 
doi:10.1155/2021/4156832 fatcat:bijpxjdexzd4tiru24u5ocxuje

Progresses and Challenges in Link Prediction [article]

Tao Zhou
2021 arXiv   pre-print
After a brief introduction of the standard problem and metrics of link prediction, this Perspective will summarize representative progresses about local similarity indices, link predictability, network  ...  Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology.  ...  Ignoring topological correlations, the randomness and thus predictability of a time series can be quantified by the entropy rate [48] . Tang et al.  ... 
arXiv:2102.11472v2 fatcat:jnqxnuhc6vg4fosuekoghkjqwa

Review on Graph Feature Learning and Feature Extraction Techniques for Link Prediction [article]

Ece C. Mutlu, Toktam A. Oghaz, Amirarsalan Rajabi, Ivan Garibay
2020 arXiv   pre-print
The problem of link prediction has recently attracted considerable attention by research community.  ...  To the best of our knowledge, this survey is the first comprehensive study that considers all of the mentioned challenges and solutions for link prediction in graphs with the improvements in the recent  ...  [81] proposed MERW to maximize the entropy of a random walk as follows: lim l→∞ − A t vx vy ∈A t p(A t vxvy ) ln p(A t vxvy ) t . (36) Here, p(A t vxvy ) is the multiplication of the iterative transition  ... 
arXiv:1901.03425v4 fatcat:o4mg2dopjrhe3kesmzfg3zegui

Learning-based link prediction analysis for Facebook100 network [article]

Tim Poštuvan, Semir Salkić, Lovro Šubelj
2021 arXiv   pre-print
This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We study performance and evaluate multiple machine learning algorithms on different feature sets.  ...  Its data contributed to significant evolution of social network research and link prediction techniques, which are important tools in link mining and analysis.  ...  For the purpose of this paper methods based on random walks are the most relevant. Methods based on random walks determine similarities using random walks on the original network.  ... 
arXiv:2008.00308v2 fatcat:6szgcsjmz5dipgnt7pxod2qymy

Link Prediction with Contextualized Self-Supervision [article]

Daokun Zhang, Jie Yin, Philip S. Yu
2022 arXiv   pre-print
Two types of structural contexts are investigated, i.e., context nodes collected from random walks vs. context subgraphs.  ...  Link prediction aims to infer the existence of a link between two nodes in a network.  ...  ACKNOWLEDGMENTS This work is supported by a joint CRP research fund between the University of Sydney and Data61, CSIRO.  ... 
arXiv:2201.10069v1 fatcat:kehwqkrqfvgdpgiimxa3gk5ali

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph [article]

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Mahsa Baktashmotlagh
2021 arXiv   pre-print
link prediction task.  ...  Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction.  ...  Thanks to Kevin Swersky for valuable discussions on this topic and to the reviewers for their helpful suggestions.  ... 
arXiv:2011.12517v2 fatcat:cbfriruejva2rf3r5of25aitz4

Gravity-Inspired Graph Autoencoders for Directed Link Prediction [article]

Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis
2019 arXiv   pre-print
In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved  ...  In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs.  ...  We trained models from 10 random walks of length 80 per node, with p = q = 1 and a window size of 5.  ... 
arXiv:1905.09570v4 fatcat:adrelbj4w5boppouarh5bdzdsq

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
They focused on generalizing the random walk with restart algorithm to the SAN framework and showed improved performance.  ...  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.  ...  Li [15] proposed Maximal Entropy Random Walk (MERW). Lichtenwalter et al. [17] proposed the PropFlow algorithm which is similar to RWwR but more localized.  ... 
arXiv:1112.3265v9 fatcat:fh2lf5njtveapf7bzim5pky6ke

Classification Using Link Prediction [article]

Seyed Amin Fadaee, Maryam Amir Haeri
2018 arXiv   pre-print
the predictive power of multiple link predictors and also exploits the low level features of the data.  ...  Link prediction in a graph is the problem of detecting the missing links that would be formed in the near future.  ...  This algorithm employs the powers of graph representation and link prediction methods in complex networks to deal with this problem 1 .  ... 
arXiv:1810.00717v1 fatcat:xjpxueyhq5c2zapfs3euhvaqa4

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
The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks.  ...  Finally, some future challenges of the link prediction in social networks are discussed.  ...  They propose a set of link prediction methods based on maximal entropy random walk, which can capture the centrality of nodes [124] .  ... 
arXiv:1411.5118v2 fatcat:ns5ufekku5hwnotjlfgj2oiaei

Learning-based link prediction analysis for Facebook100 network

Tim Poštuvan, Semir Salkić, Lovro Šubelj
2021 Uporabna informatika  
Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis.  ...  This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets.  ...  For the purpose of this paper methods based on ran-dom walks are the most relevant. Methods based on random walks determine similarities using random walks on the original network.  ... 
doi:10.31449/upinf.vol29.num2.112 fatcat:vwtzspy2wnddpalv2l6jhaeqxq
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