Search Results for author:
Found 5 papers, 4 papers with code
Graph Anomaly Detection with Unsupervised GNNs
This work aims to fill two gaps in the literature: We (1) design GLAM, an end-to-end graph-level anomaly detection model based on GNNs, and (2) focus on unsupervised model selection, which is notoriously hard due to lack of any labels, yet especially critical for deep NN based models with a long list of hyper-parameters.
Fast Attributed Graph Embedding via Density of States
We propose A-DOGE, for Attributed DOS-based Graph Embedding, based on density of states (DOS, a. k. a.
A Study of Performance of Optimal Transport
We investigate the problem of efficiently computing optimal transport (OT) distances, which is equivalent to the node-capacitated minimum cost maximum flow problem in a bipartite graph.
Faster width-dependent algorithm for mixed packing and covering LPs
As a special case of our result, we report a $1+\eps$ approximation algorithm for the densest subgraph problem which runs in time $O(md/ \eps)$, where $m$ is the number of edges in the graph and $d$ is the maximum graph degree.
ZeroER: Entity Resolution using Zero Labeled Examples
We investigate an important problem that vexes practitioners: is it possible to design an effective algorithm for ER that requires Zero labeled examples, yet can achieve performance comparable to supervised approaches?
