Network alignment provides a comprehensive way to discover the similar parts between
molecular systems of different species based on topological and biological similarity. With …

Sentiment analysis of social multimedia data has attracted extensive research interest and
has been applied to many tasks, such as election prediction and products evaluation …

Sponsored search ads appear next to search results when people look for products and
services on search engines. In recent years, they have become one of the most lucrative …

Graph alignment aims to identify node correspondence across multiple graphs, with
significant implications in various domains. As supervision information is often not available …

Abstract Multiple Instance Learning (MIL) is a fundamental method for weakly supervised
object detection (WSOD), but experiences difficulty in excluding local optimal solutions and …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …

Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative
filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Despite achieving remarkable performance, deep graph learning models, such as node
classification and network embedding, suffer from harassment caused by small adversarial …