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 …

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

Introduction Knowledge graphs have proven to be promising systems of information storage
and retrieval. Due to the recent explosion of heterogeneous multimodal data sources …

Automatic sentiment analysis of social media texts is of great significance for identifying
people's opinions that can help people make better decisions. Annotating data is time …

To date, large amounts of discriminative semantics-preserving discrete hash models are
enjoying great popularity in cross-modal hashing community. Most of them, however, distill …

Heterogeneous graph representation learning aims to learn low-dimensional vector
representations of different types of entities and relations to empower downstream tasks …

The problem of multi-domain sentiment analysis is complex since meaning of words in
different domains can be interpreted differently. This paper proposes a deep bi-directional …

Deep graph clustering approaches employ deep graph neural networks to encode node
embeddings and subsequently partition nodes based on these representations. Recent one …

In recent years, network embedding has attracted extensive interests, which aims at
representing nodes of an original network in a low-dimensional vector space while …

Network Embedding (NE) has emerged as a powerful tool in many applications. Many real-
world networks have multiple types of relations between the same entities, which are …