Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

In online social networks, users with similar interests tend to come together, forming social
communities. Nowadays, user-defined communities become a prominent part of online …

Graph representation learning (GRL) is a powerful tool for graph analysis, which has gained
massive attention from both academia and industry due to its superior performance in …

\em Personalized PageRank (PPR) stands as a fundamental proximity measure in graph
mining. Given an input graph G with the probability of decay α, a source node s and a target …

Similarity-based embedding methods have introduced a new perspective on graph
embedding by conforming the similarity distribution of latent vectors in the embedding space …

Subgraph counting, as a fundamental problem in network analysis, is to count the number of
subgraphs in a data graph that match a given query graph by either homomorphism or …

The key to vertex embedding is to learn low-dimensional representations of global graph
information, and integrating information from multiple steps is an effective strategy. Existing …

Graph alignment, also known as network alignment, has many applications in data mining
tasks. It aims to find the node correspondence across disjoint graphs. With recent …

Subset embedding is the task to learn low-dimensional representations for a subset of
nodes according to the graph topology. It has applications when we focus on a subset of …

Graph neural network (GNN) has become a popular tool to analyze the graph data. Existing
GNNs only focus on networks with first-order dependency, that is, conventional networks …