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 …

Large-scale graph-structured computation is central to tasks ranging from targeted
advertising to natural language processing and has led to the development of several graph …

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 …

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 …

In pursuit of graph processing performance, the systems community has largely abandoned
general-purpose distributed dataflow frameworks in favor of specialized graph processing …

There has been significant recent interest in parallel frameworks for processing graphs due
to their applicability in studying social networks, the Web graph, networks in biology, and …

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 …

X-Stream is a system for processing both in-memory and out-of-core graphs on a single
shared-memory machine. While retaining the scatter-gather programming model with state …

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 …