Graph is a well known data structure to represent the associated relationships in a variety of
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …

We study distributed machine learning in heterogeneous environments in this work. We first
conduct a systematic study of existing systems running distributed stochastic gradient …

There is a growing need to perform a diverse set of real-time analytics (batch and stream
analytics) on evolving graphs to deliver the values of big data to users. The key requirement …

There has been a growing interest in the graph-streaming setting where a continuous
stream of graph updates is mixed with graph queries. In principle, purely-functional trees are …

Efficient streaming graph processing systems leverage incremental processing by updating
computed results to reflect the change in graph structure for the latest graph snapshot …

Continuous processing of a streaming graph maintains an approximate result of the iterative
computation on a recent version of the graph. Upon a user query, the accurate result on the …

The value of graph-based big data can be unlocked by exploring the topology and metrics of
the networks they represent, and the computational approaches to this exploration take on …

Distributed deep learning training usually adopts All-Reduce as the synchronization
mechanism for data parallel algorithms due to its high performance in homogeneous …

State-of-the-art streaming graph processing systems that provide Bulk Synchronous Parallel
(BSP) guarantees remain oblivious to the computation sparsity present in iterative graph …

We consider the problem of graph analytics on evolving graphs (ie, graphs that change over
time). In this scenario, a query typically needs to be applied to different snapshots of the …