Results 221 to 230 of about 315,334 (257)

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
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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
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Link Prediction for Biomedical Network

The 12th International Conference on Advances in Information Technology, 2021
Network datasets are seen ubiquity in many fields, such as protein interactions, paper citation, and social networks. While some networks are well-defined, many others are not. For example, the interactions of proteins in cancer pathways are still studied by system biologists and medical researchers.
Chau Pham 0001, Tommy Dang
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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
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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
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Link prediction in citation networks

Journal of the American Society for Information Science and Technology, 2011
AbstractIn this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large‐scale datasets of citation networks. The supervised machine‐learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82.
Naoki Shibata, Yuya Kajikawa, Ichiro Sakata   +2 more
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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
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Temporal Link Prediction: A Survey

New Generation Computing, 2019
The evolutionary behavior of temporal networks has gained the attention of researchers with its ubiquitous applications in a variety of real-world scenarios. Learning evolutionary behavior of networks is directly related to link prediction problem, as the addition or removal of new links or edges over time leads to the network evolution.
Aswathy Divakaran, Anuraj Mohan
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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
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An Ensemble Approach to Link Prediction

IEEE Transactions on Knowledge and Data Engineering, 2017
A network with $n$ nodes contains $O(n^2)$ possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as ...
Duan, Liang, Ma, Shuai, Aggarwal, Charu, Ma, Tiejun, Huai, Jinpeng   +4 more
openaire   +2 more sources