Hypergraphs, which belong to the family of higher-order networks, are a natural and
powerful choice for modeling group interactions in the real world. For example, when …

In the last two decades, the continuous increase of computational power has produced an
overwhelming flow of data which has called for a paradigm shift in the computing …

There has been significant recent interest in parallel frameworks for processing graphs due
to their applicability in studying social networks, the Web graph, networks in biology, and …

We introduce a simple data model and API tailored for serving the social graph, and TAO, an
implementation of this model. TAO is a geographically distributed data store that provides …

We offer a new metric for big data platforms, COST, or the Configuration that Outperforms a
Single Thread. The COST of a given platform for a given problem is the hardware …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

The success of online social networks has attracted a constant interest in attacking and
exploiting them. Attackers usually control malicious accounts, including both fake and …

We present LLAMA, a graph storage and analysis system that supports mutability and out-of-
memory execution. LLAMA performs comparably to immutable main-memory analysis …

Internet of Things (IoT) is a structure in which objects, people are provided with exclusive
identity and the ability to relocate data over a network without requiring two way …

Graph partitioning is an essential yet challenging task for massive graph analysis in
distributed computing. Common graph partitioning methods scan the complete graph to …