Publication

Network Alignment by Representation Learning on Structure and Attribute

Chi Thang Duong, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên
2019
Conference Papers

Abstract

Network alignment is the task of recognizing similar network nodes across different networks, which has many applications in various domains. As traditional network alignment methods based on matrix factorization do not scale to large graphs, a variety of representation learning based approaches has been proposed recently. However, these techniques tend to focus on topology consistency between two networks while ignoring other valuable information (e.g. network nodes attribute), which makes them susceptible to structural changes. To alleviate this problem, we propose RAN, a representation-based network alignment model that couples both structure and node attribute information. Our framework first constructs multi-layer networks to represent topology and node attribute information, then computes the alignment result by learning the node embeddings for source and target network. The experimental results show that our method is able to outperform other techniques significantly even on large datasets.

Official source

https://infoscience.epfl.ch/entities/publication/1dd5f677-c031-4d52-8939-af247b60c9d7

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Chi Thang Duong, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên
2019
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/1dd5f677-c031-4d52-8939-af247b60c9d7

About this result

Related concepts (30)

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

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