网络表示学习算法综述

doi:10.11896/jsjkx.190300004

Abstract

Abstract: A network is a collection of nodes and edges,usually is represented as a graph.Many complex systems take the form of networks,such as social networks,biological networks,and information networks.In order to make network data processing simple and effective,the representation learning for nodes in the network has become a research hotspot in recent years.Network representation learning is designed to learn a low-dimensional dense representation vector for each node in the network that can advance various learning tasks in the network analysis area such as node classification,network clustering,and link prediction.However,most of previous works have been designed only for plain networks and ignore the node attributes.When the network is high sparsity,attributes can be the very useful complementary content to help learn better representations.Therefore,the network embedding should not only preserve the structural information,but also preserve the attribute information.In addition,in practical applications,many networks are dynamic and evolve over time with the addition,changing and deletion of nodes.Meanwhile,similar as network structure,node attributes also change naturally over time.With the development of machine learning,studies on the network representation learning emerge one after another.In this paper,we will systematically introduce and summarize the network representation learning methods in recent years.

Key words: Networks, Network representation learning, Machine learning, Network embedding, Deep learning

CLC Number:

TP181

DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning[J].Computer Science, 2020, 47(9): 52-59.

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