Temporal graph representation learning aims to develop low-dimensional embeddings for
nodes in a graph that can effectively capture their structural and temporal properties. Prior …

The k-plex is a popular definition of communities in networks, offering more flexibility than
cliques by allowing each node to miss up to k connections. However, finding k-plexes in …

Multivariate time series (MTS) anomaly detection is a critical task that involves identifying
abnormal patterns or events in data that consist of multiple interrelated time series. In order …

Real-world graphs have inherently complex and diverse topological patterns, known as
topological heterogeneity. Most existing works learn graph representation in a single …

Graph embeddings play a critical role in graph representation learning, allowing machine
learning models to explore and interpret graph-structured data. However, existing methods …

Graph neural networks (GNNs) are gaining popularity for processing graph-structured data.
In real-world scenarios, graph data within the same dataset can vary significantly in scale …

The Mixture-of-Experts (MoE) is a widely known deep neural architecture where an
ensemble of specialized sub-models (a group of experts) optimizes the overall performance …

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task
in graph machine learning. Heuristic methods, leveraging a range of different pairwise …

This study investigates the integration of Retrieval Augmented Generation (RAG) into the
Mistral 8x7B Large Language Model (LLM), which already uses Mixture of Experts (MoE), to …

Self-supervised learning in graphs has emerged as a promising avenue for harnessing
unlabeled graph data, leveraging pretext tasks to generate informative node …