Leman Akoglu
Dean's Associate Professor of Information Systems
Quick links
CONFR/JRNL
WORKSHOP
CODE
DATA
BOOK CHAP.
TECH. REPRT
THESIS
PATENTS
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Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
[Blog]
Xiyuan Zhang, Danielle C. Maddix, Junming Yin, Nick Erickson, Abdul Fatir Ansari, Boran Han, Shuai Zhang, Leman Akoglu, Christos Faloutsos, Michael W. Mahoney, Cuixiong Hu, Huzefa Rangwala, George Karypis, Bernie Wang
NeurIPS 2025, San Diego, California, USA (To appear)
[
code & data ]
Haomin Wen, Shurui Cao, Leman Akoglu
ACM SIGSPATIAL 2025, Minneapolis, MN, USA (Full paper)
(To appear)
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Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng,
Leman Akoglu, Charu C. Aggarwal
NeurIPS 2022, New Orleans, LA, USA
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SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection
[arXiv]
Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu
MLSys 2021, Virtual
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RolX: Structural Role Extraction & Mining in Large Graphs
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Lei Li, Christos Faloutsos.
ACM SIGKDD 2012, Beijing, China
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EdgeCentric: Anomaly Detection in Edge-Attributed Networks
[
code]
Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos.
IEEE ICDM Workshop on Data Mining for Cyber Security, Barcelona, Spain, Dec. 2016.
StreamSpot: finding anomalies among a collection of streaming heterogeneous graphs
CARE: a sequential ensemble algorithm for outlier detection
HINside: ranking nodes in a heterogeneous network with node-location information
EdgeCentric: finding distributional anomalies for spotting suspicious user behavior.
TemporalOpinionSpam: event detection in multivariate indicative signals of opinion spam.
SpammerGroups: computing NFS (network footprint score) and identifying groups of spammers that target the same services within the same campaign.
GRASP-RLS: finding robust subgraphs of a given size (in Matlab).
FraudEagle: finding fake reviewers in bipartite review networks (in Matlab).
CoClusLSH: fast bottom-up (agglomerative) hierarchical co-clustering (in Matlab).
RTG: generate realistic, time-evolving, weighted graphs (in Matlab).
OddBall: spot anomalous nodes
in weighted, unlabeled graphs (in Python and Matlab).
>>> Here is a Python-free 'light' version of Oddball.
PICS: partition attributed graphs into cohesive clusters (in Matlab).
Dot2Dot: summarize (by a forest of connection trees) a set of nodes in a graph (in Matlab).
CompreX: find anomalies in categorical/numerical databases (in Matlab).