Efficient Network Embedding for Large-Scale Graph Analysis

Speaker: Dr. Renchi Yang
         School of Computing
         National University of Singapore

Title:  "Efficient Network Embedding for Large-Scale Graph Analysis"

Date:   Monday, 26 September 2022

Time:   10:00am - 11:00am HKT

Zoom link:
https://hkust.zoom.us/j/465698645?pwd=aVRaNWs2RHNFcXpnWGlkR05wTTk3UT09

Meeting ID:     465 698 645
Passcode:       20222023

Abstract:

In the era of big data, a vital challenge is to efficiently and
effectively exploit graph-structured data, such as the World Wide Web,
social networks, and biological graphs. In recent years, network embedding
emerges as a powerful technique for graph analytics. Network embedding
aims to transform nodes in a graph into low-dimensional vectors, which can
be fed into off-the-shelf machine learning models used for downstream
tasks, and thus, create extensive practical applications in real life.
However, most of the existing solutions for network embedding struggle to
cope with large graphs (let's say in the order of billions of edges), as
they either incur significant computation costs or yield low-quality
embeddings on such graphs. In this talk, I will present two effective
network embedding approaches that scale to billion-edge graphs with and
without node attributes, respectively, using a single commodity machine.
The basic idea is to first model the affinity between elements in the
graph based on random walks, and then factorize the affinity matrix to
derive the embeddings. The main challenges that we address include (i) the
choice of the affinity measure and (ii) the reduction of space and time
overheads entailed by the construction and factorization of the affinity
matrix. Extensive experiments on large graphs demonstrate that our
solutions outperform existing methods in terms of both embedding quality
and efficiency.


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Biography:

Dr. Renchi Yang is now a research fellow in the School of Computing,
National University of Singapore. Prior to that, he earned his Ph.D. in
computer science from Nanyang Technological University in 2021. His
research mainly focuses on developing efficient algorithms and systems for
large-scale data analysis, with special interests in graph query
processing and graph representation learning. He has published more than
12 papers in big data-related top-tier conferences/journals including
SIGMOD, VLDB, TODS, KDD, WWW, etc. His research works have received the
VLDB 2021 Best Research Paper Award, 2022 ACM SIGMOD Research Highlight
Award, and Best Paper Award Nominee in WWW 2022.