An Overview on Network Representation Learning (original) (raw)

2019

Networks are one of the most powerful structures for modeling problems in the real world. Downstream machine learning tasks defined on networks have the potential to solve a variety of problems. With link prediction, for instance, one can predict whether two persons will become friends on a social network. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding encompasses various methods for unsupervised, and sometimes supervised, learning of feature representations of nodes and links in a network. Typically, embedding methods are based on the assumption that the similarity between nodes in the network should be reflected in the learned feature representations. In this paper, we review significant contributions to network embedding in the last decade. In particular, we look at four methods: Spectral Clustering, DeepWalk, Large-scale Information Network Embedding (LINE), and node2vec. We describe each method and list its advanta...

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A Comprehensive Comparison of Unsupervised Network Representation Learning Methods

ArXiv, 2019

There has been appreciable progress in unsupervised network representation learning (UNRL) approaches over graphs recently with flexible random-walk approaches, new optimization objectives and deep architectures. However, there is no common ground for systematic comparison of embeddings to understand their behavior for different graphs and tasks. In this paper we theoretically group different approaches under a unifying framework and empirically investigate the effectiveness of different network representation methods. In particular, we argue that most of the UNRL approaches either explicitly or implicit model and exploit context information of a node. Consequently, we propose a framework that casts a variety of approaches – random walk based, matrix factorization and deep learning based – into a unified context-based optimization function. We systematically group the methods based on their similarities and differences. We study the differences among these methods in detail which we...

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Mineral: Multi-modal Network Representation Learning

2017

Network representation learning (NRL) is a task of learning an embedding of nodes in a low-dimensional space. Recent advances in this area have achieved interesting results; however, as there is no solution that fits all kind of networks, NRL algorithms need to be specialized to preserve specific aspects of the networks, such as topology, information content, and community structure. One aspect that has been neglected so far is how a network reacts to the diffusion of information. This aspect is particularly relevant in the context of social networks. Studies have found out that diffusion reveals complex patterns in the network structure that are otherwise difficult to be discovered by other means. In this work, we describe a novel algorithm that combines topology, information content and diffusion process, and jointly learns a high quality embedding of nodes. We performed several experiments using multiple datasets and demonstrate that our algorithm performs significantly better in...

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DeepMap: Learning Deep Representations for Graph Classification

2020

Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. Graph kernels are positive-semidefinite functions that decompose graphs into substructures and compare them. One problem in the effective implementation of this idea is that the substructures are not independent, which leads to high-dimensional feature space. In addition, graph kernels cannot capture the high-order complex interactions between vertices. To mitigate these two problems, we propose a framework called DeepMap to learn deep representations for graph feature maps. The learnt deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood. DeepMap extends Convolutional Neural Networks (CNNs) to arbitrary graphs by aligning vertices across graphs and building the receptive field for each vertex. We empirically validate DeepMap on various graph classificati...

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Learning Representations of Graph Data - A Survey

ArXiv, 2019

Deep Neural Networks have shown tremendous success in the area of object recognition, image classification and natural language processing. However, designing optimal Neural Network architectures that can learn and output arbitrary graphs is an ongoing research problem. The objective of this survey is to summarize and discuss the latest advances in methods to Learn Representations of Graph Data. We start by identifying commonly used types of graph data and review basics of graph theory. This is followed by a discussion of the relationships between graph kernel methods and neural networks. Next we identify the major approaches used for learning representations of graph data namely: Kernel approaches, Convolutional approaches, Graph neural networks approaches, Graph embedding approaches and Probabilistic approaches. A variety of methods under each of the approaches are discussed and the survey is concluded with a brief discussion of the future of learning representation of graph data.

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An Overview on Network Representation Learning (original) (raw)

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