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Knowledge graphs: Opportunities and challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …
important to organize and represent the enormous volume of knowledge appropriately. As …
A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions
Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …
conventional use cases, including graphs. Graph data provides relational information …
Combating misinformation in the age of llms: Opportunities and challenges
Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …
and public trust. The emergence of large language models (LLMs) has great potential to …
Rethinking graph neural networks for anomaly detection
Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …
one of the key components for GNN design is to select a tailored spectral filter, we take the …
Combating misinformation in the era of generative AI models
Misinformation has been a persistent and harmful phenomenon affecting our society in
various ways, including individuals' physical health and economic stability. With the rise of …
various ways, including individuals' physical health and economic stability. With the rise of …
Addressing heterophily in graph anomaly detection: A perspective of graph spectrum
Graph anomaly detection (GAD) suffers from heterophily—abnormal nodes are sparse so
that they are connected to vast normal nodes. The current solutions upon Graph Neural …
that they are connected to vast normal nodes. The current solutions upon Graph Neural …
Domain-specific knowledge graphs: A survey
Abstract Knowledge Graphs (KGs) have made a qualitative leap and effected a real
revolution in knowledge representation. This is leveraged by the underlying structure of the …
revolution in knowledge representation. This is leveraged by the underlying structure of the …
Fairness in graph mining: A survey
Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …
However, despite their promising performance on various graph analytical tasks, most of …
An attentive survey of attention models
Attention Model has now become an important concept in neural networks that has been
researched within diverse application domains. This survey provides a structured and …
researched within diverse application domains. This survey provides a structured and …
Twibot-22: Towards graph-based twitter bot detection
Twitter bot detection has become an increasingly important task to combat misinformation,
facilitate social media moderation, and preserve the integrity of the online discourse. State-of …
facilitate social media moderation, and preserve the integrity of the online discourse. State-of …