DeepWalk

Introduced by Perozzi et al. in DeepWalk: Online Learning of Social Representations

DeepWalk learns embeddings (social representations) of a graph's vertices, by modeling a stream of short random walks. Social representations are latent features of the vertices that capture neighborhood similarity and community membership. These latent representations encode social relations in a continuous vector space with a relatively small number of dimensions. It generalizes neural language models to process a special language composed of a set of randomly-generated walks.

The goal is to learn a latent representation, not only a probability distribution of node co-occurrences, and so as to introduce a mapping function $\Phi \colon v \in V \mapsto \mathbb{R}^{|V|\times d}$. This mapping $\Phi$ represents the latent social representation associated with each vertex $v$ in the graph. In practice, $\Phi$ is represented by a $|V| \times d$ matrix of free parameters.

Source: DeepWalk: Online Learning of Social Representations

Latest Papers

PAPER DATE
div2vec: Diversity-Emphasized Node Embedding
Jisu JeongJeong-Min YunHongi KeamYoung-Jin ParkZimin ParkJunki Cho
2020-09-21
Force2Vec: Parallel force-directed graph embedding
| Md. Khaledur RahmanMajedul Haque SujonAriful Azad
2020-09-17
NextDoor: GPU-Based Graph Sampling for GraphMachine Learning
Abhinav JangdaSandeep PolisettyArjun GuhaMarco Serafini
2020-09-14
Concentration Bounds for Co-occurrence Matrices of Markov Chains
Jiezhong QiuChi WangBen LiaoRichard PengJie Tang
2020-08-06
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
Sudhanshu ChanpuriyaCameron Musco
2020-05-29
Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences
| Ben GoertzelMike DuncanDebbie DuongNil GeisweillerHedra SeidAbdulrahman SemrieMan Hin LeungMatthew Ikle'
2020-05-26
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines ChamiSami Abu-El-HaijaBryan PerozziChristopher RéKevin Murphy
2020-05-07
Characterizing and Detecting Money Laundering Activities on the Bitcoin Network
Yining HuSuranga SeneviratneKanchana ThilakarathnaKensuke FukudaAruna Seneviratne
2019-12-27
MANELA: A Multi-Agent Algorithm for Learning Network Embeddings
Han ZhangHong Xu
2019-12-01
Network Embedding: An Overview
Nino ArsovGeorgina Mirceva
2019-11-26
Symbolic Graph Embedding using Frequent Pattern Mining
| Blaz ŠkrljJan KraljNada Lavrač
2019-10-29
Tutorial on NLP-Inspired Network Embedding
Boaz Shmueli
2019-10-16
Distributed Training of Embeddings using Graph Analytics
Gurbinder GillRoshan DathathriSaeed MalekiMadan MusuvathiTodd MytkowiczOlli Saarikivi
2019-09-08
Adversarial Training Methods for Network Embedding
| Quanyu DaiXiao ShenLiang ZhangQiang LiDan Wang
2019-08-30
Fast and Accurate Network Embeddings via Very Sparse Random Projection
| Haochen ChenSyed Fahad SultanYingtao TianMuhao ChenSteven Skiena
2019-08-30
Network Embedding: on Compression and Learning
| Esra AkbasMehmet Aktas
2019-07-05
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
| Jiezhong QiuYuxiao DongHao MaJian LiChi WangKuansan WangJie Tang
2019-06-26
Unsupervised Euclidean Distance Attack on Network Embedding
Qi XuanJun ZhengLihong ChenShanqing YuJinyin ChenDan ZhangQingpeng Zhang Member
2019-05-27
Network Representation Learning: Consolidation and Renewed Bearing
| Saket GurukarPriyesh VijayanAakash SrinivasanGoonmeet BajajChen CaiMoniba KeymaneshSaravana KumarPranav ManerikerAnasua MitraVedang PatelBalaraman RavindranSrinivasan Parthasarathy
2019-05-02
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie SunJian TangHuichen LiBo LiChaowei XiaoYao ChenDawn Song
2018-10-30
Attention Models with Random Features for Multi-layered Graph Embeddings
Uday Shankar ShanthamalluJayaraman J. ThiagarajanHuan SongAndreas Spanias
2018-10-02
Improved Deep Embeddings for Inferencing with Multi-Layered Networks
Huan SongJayaraman J. Thiagarajan
2018-09-20
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
Liheng ChenYanru QuZhenghui WangLin QiuWeinan ZhangKen ChenShaodian ZhangYong Yu
2018-09-12
Learning Role-based Graph Embeddings
| Nesreen K. AhmedRyan RossiJohn Boaz LeeTheodore L. WillkeRong ZhouXiangnan KongHoda Eldardiry
2018-02-07
Inductive Representation Learning in Large Attributed Graphs
Nesreen K. AhmedRyan A. RossiRong ZhouJohn Boaz LeeXiangnan KongTheodore L. WillkeHoda Eldardiry
2017-10-25
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
| Jiezhong QiuYuxiao DongHao MaJian LiKuansan WangJie Tang
2017-10-09
A Framework for Generalizing Graph-based Representation Learning Methods
Nesreen K. AhmedRyan A. RossiRong ZhouJohn Boaz LeeXiangnan KongTheodore L. WillkeHoda Eldardiry
2017-09-14
HARP: Hierarchical Representation Learning for Networks
Haochen ChenBryan PerozziYifan HuSteven Skiena
2017-06-23
NEXT: A Neural Network Framework for Next POI Recommendation
Zhiqian ZhangChenliang LiZhiyong WuAixin SunDengpan YeXiangyang Luo
2017-04-15
Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings
Bryan PerozziVivek KulkarniHaochen ChenSteven Skiena
2016-05-06
GraRep: Learning Graph Representations with Global Structural Information
| Shaosheng CaoWei LuQiongkai Xu
2015-10-17
Network Representation Learning with Rich Text Information
| Cheng YangZhiyuan LiuDeli ZhaoMaosong SunEdward Chang
2015-06-24
Comprehend DeepWalk as Matrix Factorization
Cheng YangZhiyuan Liu
2015-01-02
DeepWalk: Online Learning of Social Representations
| Bryan PerozziRami Al-RfouSteven Skiena
2014-03-26

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