Results 241 to 250 of about 3,270,134 (279)

Virtual-Link Representation for Link Prediction

2019 IEEE International Conference on Big Data (Big Data), 2019
Link prediction predicts the likelihood of a future association between two nodes in a network. It plays an important role in mining and analyzing the evolution of networks and it is the fundament of many applications, including bioinformatics, e-commerce, security domain and co-authorship networks. The past few decades has witnessed the development of
Can Yao, Hai Huang, Longxuan Ma, Ze Yang 0005, Lei Zhang 0049   +4 more
openaire   +2 more sources

Link Prediction in Signed Networks

Proceedings of the 31st ACM Conference on Hypertext and Social Media, 2020
Signed networks represent the real world relationships, which are both positive or negative. Recent research works focus on either discriminative or generative based models for signed network embedding. In this paper, we propose a generative adversarial network (GAN) model for signed network which unifies generative and discriminative models to ...
Chakraborty, Roshni, Das, Ritwika, Chakraborty, Nilotpal   +2 more
openaire   +3 more sources

Internal link prediction: A new approach for predicting links in bipartite graphs

Intelligent Data Analysis, 2013
Many real-world complex networks, like actor-movie or file-provider relations, have a bipartite nature and evolve over time. Predicting links that will appear in them is one of the main approach to understand their dynamics. Only few works address the bipartite case, though, despite its high practical interest and the specific challenges it raises.
Allali, Oussama, Magnien, Clémence, Latapy, Matthieu   +2 more
openaire   +3 more sources

Link prediction in graph streams

2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016
Link prediction is a fundamental problem that aims to estimate the likelihood of the existence of edges (links) based on the current observed structure of a graph, and has found numerous applications in social networks, bioinformatics, E-commerce, and the Web. In many real-world scenarios, however, graphs are massive in size and dynamically evolving in
Peixiang Zhao 0001, Charu C. Aggarwal, Gewen He   +2 more
openaire   +1 more source

Feature selection for link prediction

Proceedings of the 5th Ph.D. workshop on Information and knowledge, 2012
Networks that model relationships in the real world have attracted much attention in the past few years. Link prediction plays a central role in the network area. Supervised learning is an important class of algorithms used to address the link prediction problem.
Ye Xu, Daniel N. Rockmore
openaire   +2 more sources

An Axiomatic Approach to Link Prediction

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Link prediction functions are important tools that are used to predict the evolution of a network, to locate hidden or surprising links, and to recommend new connections that should be formed. Multiple link prediction functions have been developed in the past.
Sara Cohen, Aviv Zohar
openaire   +2 more sources

Generative dynamic link prediction

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019
In networks, a link prediction task aims at learning potential relations between nodes to predict unknown potential linkage states. At present, most link prediction methods are used to process static networks. These methods cannot produce good prediction results for dynamic networks.
Jinyin Chen, Xiang Lin, Chenyu Jia, Yuwei Li, Yangyang Wu, Haibin Zheng, Yi Liu   +6 more
openaire   +2 more sources

Beyond Accuracy in Link Prediction

2020
Link prediction has mainly been addressed as an accuracy-targeting problem in social network analysis. We discuss different perspectives on the problem considering other dimensions and effects that the link prediction methods may have on the network where they are applied.
Javier Sanz-Cruzado, Pablo Castells
openaire   +1 more source

Predictive tests for linked changes

Statistics in Medicine, 2008
AbstractMutations may confer a survival advantage to an organism and they can also reduce their fitness. In particular, we are interested in identifying correlated changes in genomic sequences. We consider the general situation where the observed characters at two genomic positions are summarized by an r × c contingency table.
C, Ahn, F, Seillier-Moiseiwitsch, G G, Koch   +2 more
openaire   +2 more sources

A SOFT-LINK SPECTRAL MODEL FOR LINK PREDICTION

International Journal of Semantic Computing, 2009
Unsupervised spectral clustering methods can yield good performance when identifying crisp clusters with low complexity since the learning algorithm does not rely on finding the local minima of an objective function and rather uses spectral properties of the graph. Nonetheless, the performance of such approaches are usually affected by their uncertain
openaire   +2 more sources