With the rapid growth of information, recommender systems have become
in...
Community search is a personalized community discovery problem aimed at
...
The data-hungry problem, characterized by insufficiency and low-quality ...
Graph domain adaptation models are widely adopted in cross-network learn...
With an exponential increase in submissions to top-tier Computer Science...
The generalization of neural networks is a central challenge in machine
...
Graph neural networks (GNNs) have achieved remarkable success in various...
With the advancement of generative language models, the generated text h...
Multi-aspect controllable text generation aims to generate fluent senten...
Language models trained on large-scale corpora can generate remarkably f...
Dense retrieval has shown promise in the first-stage retrieval process w...
Recommender systems often suffer from popularity bias, where popular ite...
With the wide application of Large Language Models (LLMs) such as ChatGP...
The development of knowledge graph (KG) applications has led to a rising...
Despite achieving great success, graph neural networks (GNNs) are vulner...
Recently, Graph Neural Networks (GNNs) achieve remarkable success in
Rec...
Graphs consisting of vocal nodes ("the vocal minority") and silent nodes...
Company financial risk is ubiquitous and early risk assessment for liste...
Video corpus moment retrieval (VCMR) is the task of retrieving a relevan...
Current natural language understanding (NLU) models have been continuous...
Information retrieval aims to find information that meets users' needs f...
Graph contrastive learning (GCL) emerges as the most representative appr...
Unsupervised representation learning for dynamic graphs has attracted a ...
Node injection attacks against Graph Neural Networks (GNNs) have receive...
Graph representation learning plays an important role in many graph mini...
Stance detection aims to identify whether the author of a text is in fav...
Training generative adversarial networks (GANs) with limited data is val...
Pseudo-relevance feedback (PRF) has proven to be an effective query
refo...
Given a multivariate big time series, can we detect anomalies as soon as...
Text matching is a fundamental technique in both information retrieval a...
The expressive power of message passing GNNs is upper-bounded by
Weisfei...
Unsupervised style transfer models are mainly based on an inductive lear...
Information seeking is an essential step for open-domain question answer...
Node injection attack on Graph Neural Networks (GNNs) is an emerging and...
Signed networks are such social networks having both positive and negati...
Conditional generative models aim to learn the underlying joint distribu...
In recent years, graph neural networks (GNNs) have shown powerful abilit...
Recently, transformation-based self-supervised learning has been applied...
Knowledge Graph (KG) reasoning that predicts missing facts for incomplet...
Nowadays, users are encouraged to activate across multiple online social...
Money laundering (ML) is the behavior to conceal the source of money ach...
Nowadays online users prefer to join multiple social media for the purpo...
Network embedding is aimed at mapping nodes in a network into low-dimens...
Graph neural networks (GNNs) have been proven to be effective in various...
Visual Question Answering (VQA) is a challenging multimodal task to answ...
Reference expression comprehension (REC) aims to find the location that ...
Generative adversarial networks (GANs) have achieved remarkable progress...
How can we track synchronized behavior in a stream of time-stamped tuple...
Graph neural networks (GNNs) achieve remarkable success in graph-based
s...
Despite achieving strong performance in the semi-supervised node
classif...