Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Irregular workloads are typically bottlenecked by the memory system. These workloads often
use sparse data representations, eg, compressed sparse row/column (CSR/CSC), to …

Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs. The performance
of GPM workloads is bottlenecked by control flow and memory stalls. This is because of data …

Hardware accelerators for graph analytics have gained increasing interest. Vertex-centric
and edge-centric paradigms are widely used to design graph analytics accelerators …

Streaming graphs are ubiquitous in today's big data era. Prior work has improved the
performance of streaming graph workloads without taking input characteristics into account …

As graph applications become more popular and diverse, it is important to design efficient
hardware architectures that maintain the flexibility of high-level graph programming …

A variety of complex systems, including social and communication networks, financial
markets, biology, and neuroscience are modeled using temporal graphs that contain a set of …

Many application scenarios such as social network analysis and real-time financial fraud
detection involve performing batched updates and analytics on a time-evolving or streaming …

Role Mining Problem (RMP) is an important issue in RBAC design and development.
Genetic algorithm (GA) can be an effective method for solving RMP, but known usual GAs …

Computations on irregular graph structures are important for many fields, including social
sciences, bioinformatics, chemistry, medicine, cybersecurity, healthcare, web graph …