Data similarity (or distance) computation is a fundamental research topic which underpins
many high-level applications based on similarity measures in machine learning and data …

Embeddings have become a key paradigm to learn graph representations and facilitate
downstream graph analysis tasks. Existing graph embedding techniques either sample a …

Given a graph G where each node is associated with a set of attributes, attributed network
embedding (ANE) maps each node v∈ G to a compact vector Xv, which can be used in …

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for
network-structured data, aims to predict whether there exists a link between two nodes. The …

Finding both efficient and effective quantitative representations for scholars in scientific
digital libraries has been a focal point of research. The unprecedented amounts of scholarly …

Cohesive subgraph mining on attributed social networks is attracting much attention in the
realm of graph mining and analysis. Most existing studies on cohesive subgraph mining …

Attributed graphs refer to graphs where both node links and node attributes are observable
for analysis. Attributed graph embedding enables joint representation learning of node links …

Graph embeddings have become a key paradigm to learn node representations and
facilitate downstream graph analysis tasks. Many real-world scenarios such as online social …

Network embedding, which aims at learning low-dimensional representations of nodes in a
network, has drawn much attention for various network mining tasks, ranging from link …

Perfect hashing is a hash function that maps a set of distinct keys to a set of continuous
integers without collision. However, most existing perfect hash schemes are static, which …