A new method for recommendation based on embedding spectral clustering in heterogeneous networks (RESCHet)
The advancement in internet technology has enabled the use of increasingly sophisticated
data by recommendation systems to enhance their effectiveness. This data is comprised of …
data by recommendation systems to enhance their effectiveness. This data is comprised of …
Detecting communities from heterogeneous graphs: A context path-based graph neural network model
Community detection, aiming to group the graph nodes into clusters with dense inner-
connection, is a fundamental graph mining task. Recently, it has been studied on the …
connection, is a fundamental graph mining task. Recently, it has been studied on the …
A survey about community detection over On-line Social and Heterogeneous Information Networks
Abstract In modern Online Social Networks (OSNs), the need to detect users' communities
based on their interests and social connections has became a more and more important …
based on their interests and social connections has became a more and more important …
Efficient algorithms for densest subgraph discovery on large directed graphs
Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding
of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS …
of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS …
Influential community search over large heterogeneous information networks
Recently, the topic of influential community search has gained much attention. Given a
graph, it aims to find communities of vertices with high importance values from it. Existing …
graph, it aims to find communities of vertices with high importance values from it. Existing …
[HTML][HTML] Health-aware food recommendation system with dual attention in heterogeneous graphs
Recommender systems (RS) have been increasingly applied to food and health. However,
challenges still remain, including the effective incorporation of heterogeneous information …
challenges still remain, including the effective incorporation of heterogeneous information …
Explainability in graph neural networks: An experimental survey
Graph neural networks (GNNs) have been extensively developed for graph representation
learning in various application domains. However, similar to all other neural networks …
learning in various application domains. However, similar to all other neural networks …
Cohesive subgraph search over big heterogeneous information networks: Applications, challenges, and solutions
With the advent of a wide spectrum of recent applications, querying heterogeneous
information networks (HINs) has received a great deal of attention from both academic and …
information networks (HINs) has received a great deal of attention from both academic and …
Efficient size-bounded community search over large networks
The problem of community search, which aims to find a cohesive subgraph containing user-
given query vertices, has been extensively studied recently. Most of the existing studies …
given query vertices, has been extensively studied recently. Most of the existing studies …
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating
a deluge of papers that makes it hard for researchers to keep track and explore new …
a deluge of papers that makes it hard for researchers to keep track and explore new …