Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Inspired by the great success of neural networks, graph convolutional neural networks
(GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct …

Recommender systems traditionally assume that user profiles and movie attributes are
static. Temporal dynamics are purely reactive, that is, they are inferred after they are …

Advanced Persistent Threats (APTs) are difficult to detect due to their" low-and-slow" attack
patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based …

Large networks are becoming a widely used abstraction for studying complex systems in a
broad set of disciplines, ranging from social-network analysis to molecular biology and …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Diffusion models have become a new SOTA generative modeling method in various fields,
for which there are multiple survey works that provide an overall survey. With the number of …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

Recent deep learning models have moved beyond low dimensional regular grids such as
image, video, and speech, to high-dimensional graph-structured data, such as social …