Proceedings Article10.1137/1.9781611972795.94
Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction
Hisashi Kashima,Tsuyoshi Kato,Yoshihiro Yamanishi,Masashi Sugiyama,Koji Tsuda +4 more
- 01 Dec 2009
- pp 1099-1110
TL;DR: This work proposes Link Propagation as a new semi-supervised learning method for link prediction problems, where the task is to predict unknown parts of the network structure by using auxiliary information such as node similarities.
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Abstract: We propose Link Propagation as a new semi-supervised learning method for link prediction problems, where the task is to predict unknown parts of the network structure by using auxiliary information such as node similarities. Since the proposed method can fill in missing parts of tensors, it is applicable to multi-relational domains, allowing us to handle multiple types of links simultaneously. We also give a novel efficient algorithm for Link Propagation based on an accelerated conjugate gradient method.
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Citations
Link prediction via matrix factorization
Aditya Krishna Menon,Charles Elkan +1 more
- 05 Sep 2011
TL;DR: The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores, and may be combined with optional explicit features for nodes or edges, which yields better performance.
Link prediction in social networks: the state-of-the-art
TL;DR: A systematical category for link prediction techniques and problems is presented, and some future challenges of the link prediction in social networks are discussed.
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Recommendation as link prediction in bipartite graphs
Xin Li,Hsinchun Chen +1 more
- 01 Jan 2013
TL;DR: This work proposes a kernel-based recommendation approach and design a novel graph kernel that inspects customers and items related to the focal user-item pair as its context to predict whether there may be a link and proves the validity and computational efficiency of the graph kernel.
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Tensor Completion Algorithms in Big Data Analytics
TL;DR: A modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity is provided.
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A Unified Framework for Link Recommendation Using Random Walks
Zhijun Yin,Manish Gupta,Tim Weninger,Jiawei Han +3 more
- 09 Aug 2010
TL;DR: This approach estimates link relevance by using random walk algorithm on an augmented social graph with both attribute and structure information and outperforms state-of-the-art methods for link recommendation.
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