YGM is a general-purpose asynchronous distributed computing library for C++/MPI,
designed to handle the irregular data access patterns and small messages of graph …

Community detection (or clustering) in large-scale graphs is an important problem in graph
mining. Communities reveal interesting organizational and functional characteristics of a …

We study the fundamental problem of butterfly (ie,(2, 2)-bicliques) counting in bipartite
streaming graphs. Similar to triangles in unipartite graphs, enumerating butterflies is crucial …

Understanding the higher-order interactions within network data is a key objective of
network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched …

Graph neural networks lose a lot of their computing power when more network layers are
added. As a result, the majority of existing graph neural networks have a shallow depth of …

Graph Convolutional Network (GCN) has drawn considerable research attention in recent
times. Many different problems from diverse domains can be solved efficiently using GCN …

Finding cohesive subgraphs is a crucial graph analysis kernel widely used for social and
biological networks (graphs). There exist various approaches for discovering insightful …

Community detection has become an important graph analysis kernel due to the
tremendous growth of social networks and genomics discoveries. Even though there exist a …

Triangle counting in a graph is a fundamental problem and has a wide range of applications
in various domains. It is crucial in understanding the structural properties of a graph and is …

As the popularity of graph data increases, there is a growing need to count the occurrences
of subgraph patterns of interest, for a variety of applications. Many graphs are massive in …