Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …

Subgraph counting, as a fundamental problem in network analysis, is to count the number of
subgraphs in a data graph that match a given query graph by either homomorphism or …

We systematically study the query complexity of learning geodesically convex halfspaces on
graphs. Geodesic convexity is a natural generalisation of Euclidean convexity and allows …

We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in
an active learning setup. We propose GraphPart, a novel partition-based active learning …

Subgraph counting, as a fundamental problem in network analysis, is to count the number of
subgraphs in a data graph that match a given query graph by either homomorphism or …

The vast presence of bots on Twitter requires reliable and accurate bot detection methods
that differentiate legitimate bots from malicious ones. Despite the success of those methods …

Node classification is one of the core tasks on attributed graphs, but successful graph
learning solutions require sufficiently labeled data. To keep annotation costs low, active …

Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency
of machine learning models by iteratively acquiring labels of data points with the highest …

Inlight of the extensive utilization of automated machining centers, the operation and
maintenance level and efficiency of machining centers require further enhancement. In our …

Node classification on graphs is an important task in many practical domains. It usually
requires labels for training, which can be difficult or expensive to obtain in practice. Given a …