Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Consumer sentiment analysis is a recent fad for social media-related applications such as
healthcare, crime, finance, travel, and in academia. Disentangling consumer perception to …

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …

As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …

Multimodal sentiment analysis is a very actively growing field of research. A promising area
of opportunity in this field is to improve the multimodal fusion mechanism. We present a …