Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning

no code implementations • 5 Mar 2022 • Maya Kapoor, Joshua Melton, Michael Ridenhour, Mahalavanya Sriram, Thomas Moyer, Siddharth Krishnan

This lack of instrumentation severely inhibits scientific advancement in provenance graph machine learning by hindering reproducibility and limiting the availability of data that are critical for techniques like graph neural networks.

Anomaly Detection Graph Classification +2

Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks

no code implementations • 3 Mar 2022 • Joshua Melton, Michael Ridenhour, Siddharth Krishnan

In contrast to prior work, RAHMeN is a more expressive embedding framework that embraces the multi-faceted nature of nodes in such networks, producing a set of multi-embeddings that capture the varied and diverse contexts of nodes.

Community Detection Link Prediction +2

Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models

no code implementations • 24 Jul 2020 • Michael Ridenhour, Arunkumar Bagavathi, Elaheh Raisi, Siddharth Krishnan

We also analyze a multilayer network, constructed from two types of user interactions in Gab(quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users.

Network Embedding