50 citations · 126 across the 14 of their papers we have counts for
18 papers · 1 filter
Toward Unsupervised Outlier Model Selection
Yue Zhao, Sean Zhang, Leman Akoglu
Today there exists no shortage of outlier detection algorithms in the literature, yet the complementary and critical problem of unsupervised outlier model selection (UOMS) is vastl…
A Practical, Progressively-Expressive GNN
Lingxiao Zhao, Louis Härtel, Neil Shah +1
Message passing neural networks (MPNNs) have become a dominant flavor of graph neural networks (GNNs) in recent years. Yet, MPNNs come with notable limitations; namely, they are at…
Graph Anomaly Detection with Unsupervised GNNs
Lingxiao Zhao, Saurabh Sawlani, Arvind Srinivasan +1
Graph-based anomaly detection finds numerous applications in the real-world. Thus, there exists extensive literature on the topic that has recently shifted toward deep detection mo…
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments
Shuli Jiang, Robson Leonardo Ferreira Cordeiro, Leman Akoglu
How can we detect outliers, both scattered and clustered, and also explicitly assign them to respective micro-clusters, without knowing apriori how many micro-clusters exist? How c…
C-AllOut: Catching & Calling Outliers by Type
Guilherme D. F. Silva, Leman Akoglu, Robson L. F. Cordeiro
Given an unlabeled dataset, wherein we have access only to pairwise similarities (or distances), how can we effectively (1) detect outliers, and (2) annotate/tag the outliers by ty…
Fast Attributed Graph Embedding via Density of States
Saurabh Sawlani, Lingxiao Zhao, Leman Akoglu
Given a node-attributed graph, how can we efficiently represent it with few numerical features that expressively reflect its topology and attribute information? We propose A-DOGE,…