  Addressing the challenges of processing massive graphs, which are prevalent
in diverse fields such as social, biological, and technical networks, we
introduce HeiStreamE and FreightE, two innovative (buffered) streaming
algorithms designed for efficient edge partitioning of large-scale graphs.
HeiStreamE utilizes an adapted Split-and-Connect graph model and a Fennel-based
multilevel partitioning scheme, while FreightE partitions a hypergraph
representation of the input graph. Besides ensuring superior solution quality,
these approaches also overcome the limitations of existing algorithms by
maintaining linear dependency on the graph size in both time and memory
complexity with no dependence on the number of blocks of partition. Our
comprehensive experimental analysis demonstrates that HeiStreamE outperforms
current streaming algorithms and the re-streaming algorithm 2PS in partitioning
quality (replication factor), and is more memory-efficient for real-world
networks where the number of edges is far greater than the number of vertices.
Further, FreightE is shown to produce fast and efficient partitions,
particularly for higher numbers of partition blocks.
