Different from the current node-level anomaly detection task, the goal o...
Learning unbiased node representations for imbalanced samples in the gra...
Graph Neural Networks (GNNs) are de facto solutions to structural data
l...
Social Internet of Things (SIoT), a promising and emerging paradigm that...
Social events reflect the dynamics of society and, here, natural disaste...
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
As large-scale graphs become more widespread today, it exposes computati...
Automatic knowledge graph construction aims to manufacture structured hu...
Contrastive Learning (CL) has been proved to be a powerful self-supervis...
Graphs have a superior ability to represent relational data, like chemic...
Users' involvement in creating and propagating news is a vital aspect of...
Generative models have been very successful over the years and have rece...
Social networks are considered to be heterogeneous graph neural networks...
Integrating multiple online social networks (OSNs) has important implica...
DBSCAN is widely used in many scientific and engineering fields because ...
Clustering is a fundamental machine learning task which has been widely
...
Graph-structured data consisting of objects (i.e., nodes) and relationsh...
The rising popularity of online social network services has attracted lo...
Knowledge-aware methods have boosted a range of Natural Language Process...
Influence Maximization (IM), which aims to select a set of users from a
...
Graph neural networks have emerged as a leading architecture for many
gr...
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) a...
Generative adversarial network (GAN) is widely used for generalized and
...
Oriented object detection is a crucial task in computer vision. Current
...
Graph Neural Networks (GNNs) have shown promising results on a broad spe...
Artificial intelligence (AI) provides a promising substitution for
strea...
Graph Neural Networks (GNNs) have been widely studied in various graph d...
Adaptive traffic signal control plays a significant role in the construc...
Event extraction (EE), which acquires structural event knowledge from te...
Recently published graph neural networks (GNNs) show promising performan...
This paper considers the problem of minimizing a convex expectation func...
Anomalies represent rare observations (e.g., data records or events) tha...
With the rising demand of smart mobility, ride-hailing service is gettin...
User cold-start recommendation is a long-standing challenge for recommen...
Social events provide valuable insights into group social behaviors and
...
Graph representation learning has attracted increasing research attentio...
Knowledge Graph (KG) has attracted more and more companies' attention fo...
While numerous approaches have been developed to embed graphs into eithe...
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR)...
Social media sites are now becoming very important platforms for product...
With the rapid development of mobile Internet technology and the widespr...
As communities represent similar opinions, similar functions, similar
pu...
Predicting pairs of anchor users plays an important role in the cross-ne...
Domain adaptation refers to the process of learning prediction models in...
Brain Electroencephalography (EEG) classification is widely applied to
a...
How to maintain relative high diversity is important to avoid premature
...
In distributed evolutionary algorithms, migration interval is used to de...
Positive instance detection, especially for these in positive bags (true...
In this article, how word embeddings can be used as features in Chinese
...