Learning spectral graph transformations for link prediction

Kunegis, Jérôme; Lommatzsch, Andreas

doi:10.1145/1553374.1553447

Cited by 137 publications

(79 citation statements)

References 15 publications

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“…A decision tree based link predictor that combines multiple proximity measures has been studied in [40]. There has also been some recent work on using supervised learning methods for link prediction in diverse networks such as hyperlink and citation graphs [19]. As shown in Section 5.5, our supervised link prediction technique can achieve good performance in link prediction accuracy without requiring network-specic parameter congurations.…”

Section: Related Workmentioning

confidence: 99%

Clustered embedding of massive social networks

Song

Savas

Cho

et al. 2012

SIGMETRICS Perform. Eval. Rev.

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-The explosive growth of social networks has created numerous exciting research opportunities. A central concept in the analysis of social networks is a proximity measure, which captures the closeness or similarity between nodes in the network. Despite much research on proximity measures, there is a lack of techniques to efciently and accurately compute proximity measures for largescale social networks. In this paper, we embed the original massive social graph into a much smaller graph, using a novel dimensionality reduction technique termed Clustered Spectral Graph Embedding. We show that the embedded graph captures the essential clustering and spectral structure of the original graph and allow a wide range of analysis to be performed on massive social graphs. Applying the clustered embedding to proximity measurement of social networks, we develop accurate, scalable, and exible solutions to three important social network analysis tasks: proximity estimation, missing link inference, and link prediction. We demonstrate the effectiveness of our solutions to the tasks in the context of large real-world social network datasets: Flickr, LiveJournal, and MySpace with up to 2 million nodes and 90 million links.

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Section: Related Workmentioning

confidence: 99%

Clustered embedding of massive social networks

Song

Savas

Cho

et al. 2012

SIGMETRICS Perform. Eval. Rev.

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“…A few examples are information retrieval using latent semantic indexing [11,4], link prediction, and affiliation recommendation in social networks [26,22,27,35,34,36,32]. A particular interest in network applications is to analyze network features as centrality, communicability, and betweenness.…”

Section: Motivationmentioning

confidence: 99%

Clustered Matrix Approximation

Savas¹,

Dhillon²

2016

SIAM J. Matrix Anal. & Appl.

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Abstract. In this paper we develop a novel clustered matrix approximation framework, first showing the motivation behind our research. The proposed methods are particularly well suited for problems with large scale sparse matrices that represent graphs and/or bipartite graphs from information science applications. Our framework and resulting approximations have a number of benefits: (1) the approximations preserve important structure that is present in the original matrix; (2) the approximations contain both global-scale and local-scale information; (3) the procedure is efficient both in computational speed and memory usage; and (4) the resulting approximations are considerably more accurate with less memory usage than truncated SVD approximations, which are optimal with respect to rank. The framework is also quite flexible as it may be modified in various ways to fit the needs of a particular application. In the paper we also derive a probabilistic approach that uses randomness to compute a clustered matrix approximation within the developed framework. We further prove deterministic and probabilistic bounds of the resulting approximation error. Finally, in a series of experiments we evaluate, analyze, and discuss various aspects of the proposed framework. In particular, all the benefits we claim for the clustered matrix approximation are clearly illustrated using real-world and large scale data.

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“…Interestingly, paths in a network are also connected to powers of a matrix, similar to eigenvectors. For example, an entry a t ij in the t-th power of the adjacency matrix A denotes the number of paths of length t from vertex i to vertex j [110]. It has been shown that many popular graph algorithms such as shortest paths, connected components, reachability or transitive closure can be computed by iterative matrix vector multiplications on matrices defined over certain semirings (E, ⊕, ⊗).…”

Section: Computation Via Generalized Iterative Matrix Vector Multiplimentioning

confidence: 99%

Scaling data mining in massively parallel dataflow systems

Schelter

2014

Proceedings of the 2014 SIGMOD PhD Symposium

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Learning spectral graph transformations for link prediction

Cited by 137 publications

References 15 publications

Clustered embedding of massive social networks

Clustered embedding of massive social networks

Clustered Matrix Approximation

Scaling data mining in massively parallel dataflow systems

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