Proceedings Article10.1109/ACIT.2018.8672689
Learning Graph Representation: A Comparative Study
Wael Etaiwi,Arafat Awajan +1 more
- 01 Nov 2018
- pp 1-6
4
TL;DR: This paper summarizes the recent techniques and methods used for graph representation learning, and compared them together, to raise the need for comparing the existing methods in terms of methodology and techniques.
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Abstract: Graphs are ubiquitous and allow us to model entities and the relationships between them. Graph data is often observed directly in the natural world (e.g., telecommunication or social networks), and the success of many machine learning tasks such as classification, link prediction, and many others, depends mainly on learning a useful feature representation from graph. This study investigates several research studies that have been conducted in the field of graph representation learning. The growing attention in graph representation learning and graph embedding in recent years raise the need for comparing the existing methods in terms of methodology and techniques. This paper summarizes the recent techniques and methods used for graph representation learning, and compare them together.
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Citations
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
TL;DR: In this article, the authors formalize the general mechanisms (e.g., random walks and feature diffusion) that give rise to community or role-based structural embeddings, and theoretically prove that embedding methods based on these mechanisms result in either community- or role based structural embedding.
89
Role-based Graph Embeddings
Nesreen K. Ahmed,Ryan A. Rossi,John Boaz Lee,Theodore L. Willke,Rong Zhou,Xiangnan Kong,Hoda Eldardiry +6 more
TL;DR: The Role2Vec framework is introduced, based on the proposed notion of attributed random walks to learn structural role-based embeddings, which enables these methods to be more widely applicable by learning inductive functions that capture the structural roles in the graph.
86
Role-Based Graph Embeddings
TL;DR: RoleRole2Vec as mentioned in this paper is a framework based on the notion of attributed random walks to learn structural role-based embeddings, which is shown to be effective with an average AUC improvement of 17.8 percent for link prediction.
9
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
TL;DR: In this article, the authors formalize the general mechanisms (e.g., random walks, feature diffusion) that give rise to community or role-based structural embeddings, and theoretically prove that embedding methods based on these mechanisms result in either community (based on structural similarity) or role based structural embedding.
References
•Proceedings Article
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov,Kai Chen,Greg S. Corrado,Jeffrey Dean +3 more
- 16 Jan 2013
TL;DR: Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.
27.5K
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
DeepWalk: online learning of social representations
Bryan Perozzi,Rami Al-Rfou,Steven Skiena +2 more
- 24 Aug 2014
TL;DR: DeepWalk as mentioned in this paper uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences, which encode social relations in a continuous vector space, which is easily exploited by statistical models.
node2vec: Scalable Feature Learning for Networks
Aditya Grover,Jure Leskovec +1 more
- 13 Aug 2016
TL;DR: Node2vec as mentioned in this paper learns a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes by using a biased random walk procedure.