This paper presents four novel parallel algorithms for maintaining a rooted spanning forest in dynamic graphs, achieving a throughput of 2 million insertions and 1.4 million deletions per second.

This paper studies stochastic multi-armed bandits on dynamic graphs with local movement constraints and establishes sublinear expected regret.

The paper proposes Dual-Scale Retentive Dynamics (DSRD), a unified framework that improves representation learning on dynamic graphs by jointly modeling evolving temporal and structural dependencies.

This paper proposes SG-JEPA, a method for learning predictive embeddings for large-scale dynamic graphs by partitioning nodes into context and target sets and encoding sequential inputs into spike cou…

This paper investigates the use of spectral filtering for continuous subgraph matching over dynamic graphs and presents three key findings.

SWORD is a novel online regime detection method for dynamic networks that significantly improves change point detection accuracy by comparing the mean of spectral moments between adjacent time windows…

This paper characterizes stabilizability of stochastic dynamic matching on hypergraphs using the arrival rates and incidence matrix, and proposes a stabilizing policy.

A new polynomial-time algorithm is presented for optimally coloring perfect graphs using graph-theoretic operations and iterative walk counting.

This paper introduces ZipLine, a system for integrative analysis of multivariate graphs through a unified predicate language and learning algorithm.

The paper presents an algorithm for updating a Directed Minimum Spanning Tree using the weighted matroid intersection algorithm and a dynamic auxiliary graph.

This paper models the changes in graph adjacency matrices over time as a low-rank matrix and develops a method for jointly interpolating signals and estimating graph adjacency matrices using this assu…

This paper presents an optimized algorithm for computing cut sets of a path set in graph theory and introduces a vectorized computational framework for property calculations.

This paper introduces DiPhon, a diffusion framework for size-scalable graph generation, using a continuous diffusion process on the graphon space and a discretized graph-level process.

The authors map the problem of finding the maximum cut of a planar graph with arbitrary weights to a minimum T-join problem in the absolute dual graph, enabling the use of shortest paths and adapting…