2 research outputs found
Sorting with GPUs: A Survey
Sorting is a fundamental operation in computer science and is a bottleneck in
many important fields. Sorting is critical to database applications, online
search and indexing,biomedical computing, and many other applications. The
explosive growth in computational power and availability of GPU coprocessors
has allowed sort operations on GPUs to be done much faster than any
equivalently priced CPU. Current trends in GPU computing shows that this
explosive growth in GPU capabilities is likely to continue for some time. As
such, there is a need to develop algorithms to effectively harness the power of
GPUs for crucial applications such as sorting
Network Embedding with Completely-imbalanced Labels
Network embedding, aiming to project a network into a low-dimensional space,
is increasingly becoming a focus of network research. Semi-supervised network
embedding takes advantage of labeled data, and has shown promising performance.
However, existing semi-supervised methods would get unappealing results in the
completely-imbalanced label setting where some classes have no labeled nodes at
all. To alleviate this, we propose two novel semi-supervised network embedding
methods. The first one is a shallow method named RSDNE. Specifically, to
benefit from the completely-imbalanced labels, RSDNE guarantees both
intra-class similarity and inter-class dissimilarity in an approximate way. The
other method is RECT which is a new class of graph neural networks. Different
from RSDNE, to benefit from the completely-imbalanced labels, RECT explores the
class-semantic knowledge. This enables RECT to handle networks with node
features and multi-label setting. Experimental results on several real-world
datasets demonstrate the superiority of the proposed methods.Comment: A preliminary version of this work was accepted in AAAI 2018. This
version has been accepted in IEEE Transactions on Knowledge and Data
Engineering (TKDE) 2020. Project page:
https://zhengwang100.github.io/project/zero_shot_graph_embedding.htm