Renchi Yang

Renchi Yang

Postdoctoral Researcher

National University of Singapore

Biography

Renchi Yang (杨任驰) is currently a Research Fellow at NUS. He obtained his B.E. from BUPT, and received his Ph.D. in computer science from NTU, advised by Prof. Xiaokui XIAO and Prof. Sourav Saha Bhowmick. His general research area is large-scale data management and analytics. He is currently interested in graph mining and learning, as well as designing efficient algorithms for massive high-dimensional data.

Acceptance builds CVs, rejection builds men.

Interests

  • Data management and analytics
  • Graph mining and learning
  • Parallel and distributed computing

Education

  • Doctor of Philosophy, 2020

    Nanyang Technological University

  • Bachelor of Engineering, 2015

    Beijing University of Posts and Telecommunications

News

  • May 15, 2021: One paper “Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization” has been accepted by VLDB 2021.
  • Jan 16, 2021: One paper “Effective and Scalable Clustering on Massive Attributed Graphs” has been accepted by TheWebConf(WWW) 2021.
  • Dec 15, 2020: My doctoral thesis titled “Efficient and Scalable Techniques for PageRank-based Graph Analytics” is now publicly available on Digital Repository of NTU.
  • Aug 16, 2020: One paper “Scaling Attributed Network Embedding to Massive Graphs” has been accepted by VLDB 2021.

Academic Services

  • Journal reviewer of TKDE 2021.
  • External reviewer of WWW 2021.
  • External reviewer of ICDE 2020, VLDB 2020.
  • External reviewer of KDD 2019, IJCAI 2019, CIKM 2019.

Publications

(2021). Effective and Scalable Clustering on Massive Attributed Graphs. Proceedings of The Web Conference 2021.

PDF Code DOI

(2021). Scaling Attributed Network Embedding to Massive Graphs. Proceedings of the VLDB Endowment.

PDF Code DOI

(2021). Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization. Proceedings of the VLDB Endowment.

PDF DOI

(2020). Efficient and Scalable Techniques for PageRank-based Graph Analytics. Doctoral thesis, Digital Repository of NTU.

PDF DOI

(2020). Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank. Proceedings of the VLDB Endowment.

PDF Code Slides DOI

(2020). Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs. Proceedings of the VLDB Endowment.

PDF Code DOI

(2019). Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries. ACM Transaction on Database Systems.

PDF Code DOI

(2019). Efficient Estimation of Heat Kernel PageRank for Local Clustering. Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30 - July 5, 2019.

PDF Code Poster Slides DOI

(2019). Realtime Top-k Personalized PageRank over Large Graphs on GPUs. Proceedings of the VLDB Endowment.

PDF Code DOI

(2017). FORA: Simple and Effective Approximate Single-Source Personalized PageRank. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017.

PDF Code Poster Slides Video DOI

Datasets

Plain Graphs

Name#nodes#edges#labelsTypeURL
Youtube1,138,4992,990,44347undirected[raw] [preprocessed]
TWeibo2,320,89550,655,143100directed[raw] [preprocessed]
Orkut3,072,441117,185,084100undirected[raw] [preprocessed]
In-20041,382,90816,539,643-directed[raw] [preprocessed]
DBLP5,425,96317,298,032-undirected[raw] [preprocessed]
Pokec1,632,80330,622,564-directed[raw] [preprocessed]
LiveJournal4,847,57168,475,391-directed[raw] [preprocessed]
IT-200441,291,5941,135,718,909-directed[raw] [preprocessed]
Twitter41,652,2301,468,365,182-directed[raw] [preprocessed]
Friendster65,608,3661,806,067,135-undirected[raw] [preprocessed]
UK-2007105,896,5553,738,733,648-directed[raw] [preprocessed]
UK-union133,633,0405,475,109,924-directed[raw] [preprocessed]
ClueWeb12978,408,09842,574,107,469-directed[raw]
ClueWeb091,684,868,3227,939,635,651-directed[raw] [preprocessed]

Welcome to cite our paper if you publish results based on our preprocessed datasets.

@article{yang13homogeneous,
  title={Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank},
  author={Yang, Renchi and Shi, Jieming and Xiao, Xiaokui and Yang, Yin and Bhowmick, Sourav S},
  journal={Proceedings of the VLDB Endowment},
  volume={13},
  number={5}
}

@article{shi13realtime,
  title={Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs},
  author={Shi, Jieming and Jin, Tianyuan and Yang, Renchi and Xiao, Xiaokui and Yang, Yin},
  journal={Proceedings of the VLDB Endowment},
  volume={13},
  number={7}
}

Attributed Graphs

NameType#nodes#edges#attributes#labelsURL
Wikidirected240517981497319[raw] [preprocessed]
Coradirected2708542914337[raw] [preprocessed]
Citeseerdirected3312466037036[raw] [preprocessed]
Pubmeddirected19717443385003[raw] [preprocessed]
BlogCatalogundirected519634348681896[raw] [preprocessed]
PPIundirected5694481871650121[raw] [preprocessed]
Redditundirected2329651160691930041[raw] [preprocessed]
Flickrundirected7575479476120479[raw] [preprocessed]
Facebookundirected4039882341283193[raw] [preprocessed]
Twitterdirected8130617681492168394065[raw] [preprocessed]
Google+directed1076141367345315907468[raw] [preprocessed]
TWeibodirected23208955065514316578[raw] [preprocessed]
MAGdirected592497199781472532000100[raw] [preprocessed]
MAG-SCdirected1054156026521999427842408[raw] [preprocessed]

Tips: node attributes in our preprocessed datasets are compressed as “attrs.pkl” file via cPickle package in Python 2.7 or “attrs.npz” file, which can be loaded as a sparse attribute matrix by using the following code

import cPickle as pickle
features = pickle.load(open("attrs.pkl"))

or

from scipy import sparse
features = sparse.load_npz("attrs.npz")

Welcome to cite our paper if you publish results based on our preprocessed datasets.

@article{yang2020scaling,
  title={Scaling Attributed Network Embedding to Massive Graphs},
  author={Yang, Renchi and Shi, Jieming and Xiao, Xiaokui and Yang, Yin and Liu, Juncheng and Bhowmick, Sourav S},
  journal={Proceedings of the VLDB Endowment},
  volume={14},
  number={1},
  pages={37--49},
  year={2021},
  publisher={VLDB Endowment}
}

Misc