no code implementations • 3 Apr 2024 • Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu
A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.)
1 code implementation • 12 Feb 2024 • Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers.
1 code implementation • 8 Feb 2024 • Meng-Chieh Lee, Lingxiao Zhao, Leman Akoglu
In this paper, we first revisit the RWK and its current usage in KCNs, revealing several shortcomings of the existing designs, and propose an improved graph kernel RWK+, by introducing color-matching random walks and deriving its efficient computation.
2 code implementations • 6 Feb 2024 • Lingxiao Zhao, Xueying Ding, Lijun Yu, Leman Akoglu
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs.
1 code implementation • 6 Feb 2024 • Lingxiao Zhao, Xueying Ding, Leman Akoglu
Current graph diffusion models generate graphs in a one-shot fashion, but they require extra features and thousands of denoising steps to achieve optimal performance.
1 code implementation • 13 Nov 2023 • Konstantinos Sotiropoulos, Lingxiao Zhao, Pierre Jinghong Liang, Leman Akoglu
Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances?
no code implementations • 28 Aug 2023 • Leman Akoglu, Jaemin Yoo
Self-supervised learning (SSL) is a growing torrent that has recently transformed machine learning and its many real world applications, by learning on massive amounts of unlabeled data via self-generated supervisory signals.
no code implementations • 20 Jul 2023 • Xueying Ding, Yue Zhao, Leman Akoglu
Outlier detection (OD) finds many applications with a rich literature of numerous techniques.
1 code implementation • 13 Jul 2023 • Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu
DSV captures the alignment between an augmentation function and the anomaly-generating mechanism with surrogate losses, which approximate the discordance and separability of test data, respectively.
no code implementations • 21 Jun 2023 • Jaemin Yoo, Lingxiao Zhao, Leman Akoglu
The first is a new unsupervised validation loss that quantifies the alignment between the augmented training data and the (unlabeled) test data.
no code implementations • 6 Apr 2023 • Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few.
no code implementations • 5 Nov 2022 • Shubhranshu Shekhar, Jetson Leder-Luis, Leman Akoglu
The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government.
1 code implementation • 3 Nov 2022 • Yue Zhao, Sean Zhang, Leman Akoglu
At its core, ELECT is based on meta-learning; transferring prior knowledge (e. g. model performance) on historical datasets that are similar to the new one to facilitate UOMS.
1 code implementation • 18 Oct 2022 • Lingxiao Zhao, Louis Härtel, Neil Shah, Leman Akoglu
Our model is practical and progressively-expressive, increasing in power with k and c. We demonstrate effectiveness on several benchmark datasets, achieving several state-of-the-art results with runtime and memory usage applicable to practical graphs.
1 code implementation • 18 Oct 2022 • Lingxiao Zhao, Saurabh Sawlani, Arvind Srinivasan, Leman Akoglu
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.
1 code implementation • 15 Oct 2022 • Shuli Jiang, Robson Leonardo Ferreira Cordeiro, Leman Akoglu
How can we perform both tasks in-house, i. e., without any post-hoc processing, so that both detection and assignment can benefit simultaneously from each other?
no code implementations • 24 Aug 2022 • Yue Zhao, Leman Akoglu
Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels?
1 code implementation • 16 Aug 2022 • Jaemin Yoo, Tiancheng Zhao, Leman Akoglu
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling.
1 code implementation • 15 Jun 2022 • Xueying Ding, Lingxiao Zhao, Leman Akoglu
Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains.
no code implementations • NeurIPS 2021 • Yue Zhao, Ryan Rossi, Leman Akoglu
Given an unsupervised outlier detection task on a new dataset, how can we automatically select a good outlier detection algorithm and its hyperparameter(s) (collectively called a model)?
no code implementations • 11 Nov 2021 • Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi, Jonathan Elmer, Christos Faloutsos, Leman Akoglu
Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible?
no code implementations • 13 Oct 2021 • Guilherme D. F. Silva, Leman Akoglu, Robson L. F. Cordeiro
It is parameter-free and scalable, besides working only with pairwise similarities (or distances) when it is needed.
1 code implementation • 11 Oct 2021 • Saurabh Sawlani, Lingxiao Zhao, Leman Akoglu
We propose A-DOGE, for Attributed DOS-based Graph Embedding, based on density of states (DOS, a. k. a.
2 code implementations • ICLR 2022 • Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah
We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN.
Ranked #16 on Graph Property Prediction on ogbg-molpcba
no code implementations • 29 Sep 2021 • Lingxiao Zhao, Leman Akoglu
Based on this connection, the GCN architecture, shaped by stacking graph convolution layers, shares a close relationship with stacking GPCA.
1 code implementation • 14 Jun 2021 • Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
no code implementations • 21 May 2021 • Leman Akoglu
Anomaly mining is an important problem that finds numerous applications in various real world domains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few.
1 code implementation • 3 Apr 2021 • Martin Q. Ma, Yue Zhao, Xiaorong Zhang, Leman Akoglu
These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models.
1 code implementation • 23 Dec 2020 • Lingxiao Zhao, Leman Akoglu
We carefully study the graph embedding space produced by propagation based models and find two driving factors: (1) disparity between within-class densities which is amplified by propagation, and (2)overlapping support (mixing of embeddings) across classes.
1 code implementation • 5 Dec 2020 • Shubhranshu Shekhar, Neil Shah, Leman Akoglu
Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population.
1 code implementation • 1 Nov 2020 • Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre Jinghong Liang, Leman Akoglu, Vincent S. Tseng, Christos Faloutsos
How can we spot money laundering in large-scale graph-like accounting datasets?
Social and Information Networks
no code implementations • 7 Oct 2020 • Hung T. Nguyen, Pierre J. Liang, Leman Akoglu
Within a large database G containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs?
1 code implementation • 22 Sep 2020 • Yue Zhao, Ryan A. Rossi, Leman Akoglu
Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)?
no code implementations • 22 Jun 2020 • Lingxiao Zhao, Leman Akoglu
Based on this connection, GCN architecture, shaped by stacking graph convolution layers, shares a close relationship with stacking GPCA.
4 code implementations • NeurIPS 2020 • Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i. e., in networks where connected nodes may have different class labels and dissimilar features.
1 code implementation • 11 Mar 2020 • 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
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
no code implementations • 6 Nov 2019 • Yue Wu, Leman Akoglu, Ian Davidson
Existing algorithms are primarily focused on detection, that is the identification of outliers in a given dataset.
1 code implementation • ICLR 2020 • Lingxiao Zhao, Leman Akoglu
The performance of graph neural nets (GNNs) is known to gradually decrease with increasing number of layers.
1 code implementation • 26 Sep 2019 • Xuan Wu, Lingxiao Zhao, Leman Akoglu
As such, PG-learn is a carefully-designed hybrid of random and adaptive search.
1 code implementation • NeurIPS 2019 • Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and unsupervised learning problems.
1 code implementation • 28 Jun 2019 • Hung Nguyen, Xuejian Wang, Leman Akoglu
We introduce RaRecognize, which (i) estimates a general decision boundary between the rare and the majority class, (ii) learns to recognize individual rare subclasses that exist within the training data, as well as (iii) flags instances from previously unseen rare subclasses as newly emerging.
no code implementations • 6 May 2018 • Shubhranshu Shekhar, Leman Akoglu
We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information - data available only for training examples but not for (future) test examples.
1 code implementation • 20 Aug 2017 • Meghanath Macha, Leman Akoglu
We consider a complementary problem that has a much sparser literature: anomaly description.
no code implementations • 18 Sep 2016 • Shebuti Rayana, Wen Zhong, Leman Akoglu
In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing error through both bias and variance.
no code implementations • 19 Nov 2015 • Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
no code implementations • 19 Oct 2015 • Junting Ye, Leman Akoglu
We provide a careful analysis of the inferred weights, based on which we devise an algorithm that filters out irrelevant and noisy graphs and produces weights proportional to the informativeness of the remaining graphs.
no code implementations • 8 Jan 2015 • Shebuti Rayana, Leman Akoglu
In this work, we tap into this gap and propose a new ensemble approach for anomaly mining, with application to event detection in temporal graphs.
1 code implementation • 18 Apr 2014 • Leman Akoglu, Hanghang Tong, Danai Koutra
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.
Social and Information Networks Cryptography and Security
no code implementations • 27 Nov 2013 • Yuan Yao, Hanghang Tong, Tao Xie, Leman Akoglu, Feng Xu, Jian Lu
Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge.