Community question answering aims to connect queries and answers based on users'
community behaviors, find the most relevant solutions for newly raised questions, and …

In recent years, several new Artificial Intelligence methods have been developed to make
models more explainable and interpretable. The techniques essentially deal with the …

To solve the information overload problem of multimodal answers in community question
answering (CQA), this paper proposes a multimodal representative answer extraction …

Given a heterogeneous information network (HIN) G and a query node q, community search
(CS) over an HIN identifies a cohesive subgraph from G that contains q. Although HINs with …

Phrases represent independent semantics in natural language but usually have
indeterminate lengths and different combinations. So, extracting meaningful phrases from …

Graph neural networks (GNNs) can capture interdependencies between data with the
structured data modeling ability, and have received much attention from industry …

Exploiting heterogeneous information in attributed networks to improve the performance of
community detection has attracted considerable research attention. Although variational …

With the increasing popularity of recommendation techniques and social networks, social
network recommendation has became a significant research field, ie, predicting a user's …

Heterogeneous temporal network embedding aims to learn each node of different types of a
heterogeneous temporal network in each snapshot into a low-dimensional vector …

Community search (CS) is a vital research area in network science that focuses on
discovering personalized communities for query vertices from graphs. However, existing CS …