PATH LEARNING IN COMPLEX NETWORKS

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "PATH LEARNING IN COMPLEX NETWORKS"

By

Miss Wenyi XIAO


Abstract

Graph representation learning (GRL) aims to encode the nodes and edges in 
complex networks into low dimensional vector space while preserving the 
inherent structure of the graphs. To learn better representations, three 
aspects of GRL, i.e., structural information in homogeneous graphs, relational 
information in heterogeneous graphs, and GRL to build side information for 
certain applications, e.g. recommendation systems, are extensively studied in 
the literature. However, sometimes two nodes in complex networks are located 
far away from each other. It is difficult for the GRL based models to capture 
the pairwise relationship with long distances. Besides, it would suffer from 
memory explosion if we aggregate all the high-order neighbors' information in 
graphs. Hence, we use path learning to select high-order neighbors to enrich 
the information for the target nodes. Recently, path-based models have 
exhibited their power in many machine learning tasks. Inspired by the analysis 
of paths in both academia and industry, we propose path learning in complex 
networks in this thesis proposal to address the three aspects of GRL in 
different ways.

In detail, we propose Reinforce2vec, a biased random walk based approach 
for network embedding on homogeneous graphs. Our model uses a 
non-Markovian process to fully use the history of a random walk path. 
Second, to capture the inductive bias for learning PathSim based 
similarity scores, we propose NeuPath to identify a fixed number of path 
instances that can best infer the target meta-path(s) in heterogeneous 
information networks. Third, we study how the structural social 
information learned by path learning could be applied as the side 
information for the news recommendation task. We design an MCTS based 
method to explore high-order friends for the target user. A personalized 
hierarchical attention network is proposed for news recommendation on 
decentralized platforms. Besides, we construct a variant model for more 
industry-driven applications. Extensive experiments demonstrate the 
effectiveness and efficiency of the path learning in various complex 
networks.


Date:			Wednesday, 15 December 2021

Time:			3:00pm - 5:00pm

Venue:			Room 1409
 			Lifts 25/26

Chairperson:		Prof. Chi Ying TSUI (ISD)

Committee Members:	Prof. Yangqiu SONG (Supervisor)
 			Prof. Raymond WONG
 			Prof. Nevin ZHANG
 			Prof. Can YANG (MATH)
 			Prof. Sinno Jialin PAN (Nanyang Technological Univ)


**** ALL are Welcome ****