Most Graph Neural Networks follow the message-passing paradigm, assuming observed structure depicts ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are incomplete, noisy, or redundant. How to reveal inherent graph in a unified way remains under-explored. We proposed PRI-GSL, Structure Learning framework guided by Principle o...

Online social networks, such as Facebook, Twitter, LinkedIn, etc., have grown exponentially in recent times with a large amount of information. These networks huge volumes data especially structured, textual, and unstructured forms which often led to cyber-crimes like cyber terrorism, bullying, extracting information from these has now become serious challenge order ensure the safety. In this w...

• Web knowledge-based query expansion technique uses the top N pseudo relevant web pages Top returned by search engines Google, Bing, and DuckDuckGo as data sources Proposed weighting techniques have properly capture term relationship while weighing terms Experimental results show an improvement of 25.89% over unexpanded queries FIRE dataset In field information retrieval, (QE) has long been us...