Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

With the development of social applications, users began to participate in multiple online
social networks. If the same user in different social networks can be identified, it can provide …

Given the heterogeneity of real-world networks and the low efficiency of directly mining
networks, heterogeneous information network (HIN) representation learning, which learns …

The heterogeneity and diversity of users and external knowledge resources is a hallmark of
open innovation communities (OICs). Although user segmentation in heterogeneous OICs is …

This survey aims to provide an overview of the recent developments and applications of
Graph Neural Networks (GNNs) in the field of traffic patterns recognition. The focus is on the …

The continuous detection of emotional states has many applications in mental health,
marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA) …

Code clone detection is about finding out similar code fragments, which has drawn much
attention in software engineering since it is important for software maintenance and …

Measuring the similarity between legal documents to find prior documents from a massive
collection that are similar to a current document is an essential component in legal assistant …

Recently, unsupervised graph representation learning has attracted considerable attention
through effectively encoding graph-structured data without semantic annotations. To …

Data-driven decision making is becoming an integral part of manufacturing companies. Data
is collected and commonly used to improve efficiency and produce high quality items for the …