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Found 50 papers, 27 papers with code
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
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.)
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers.
Descriptive Kernel Convolution Network with Improved Random Walk Kernel
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.
Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
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.
Improving and Unifying Discrete&Continuous-time Discrete Denoising Diffusion
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs.
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
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?
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities
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.
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
Outlier detection (OD) finds many applications with a rich literature of numerous techniques.
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
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.
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection
The first is a new unsupervised validation loss that quantifies the alignment between the augmented training data and the (unlabeled) test data.
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few.
Unsupervised Machine Learning for Explainable Health Care Fraud Detection
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.
Toward Unsupervised Outlier Model Selection
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.
A Practical, Progressively-Expressive GNN
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.
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.
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments
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?
Hyperparameter Optimization for Unsupervised Outlier Detection
Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels?
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
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.
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains.
Automatic Unsupervised Outlier Model Selection
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)?
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
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?
C-AllOut: Catching & Calling Outliers by Type
It is parameter-free and scalable, besides working only with pairwise similarities (or distances) when it is needed.
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.
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
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
Connecting Graph Convolution and Graph PCA
Based on this connection, the GCN architecture, shaped by stacking graph convolution layers, shares a close relationship with stacking GPCA.
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
Anomaly Mining -- Past, Present and Future
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.
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models.
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights
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.
FairOD: Fairness-aware Outlier Detection
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.
AutoAudit: Mining Accounting and Time-Evolving Graphs
How can we spot money laundering in large-scale graph-like accounting datasets?
Social and Information Networks
Anomaly Detection in Large Labeled Multi-Graph Databases
Within a large database G containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs?
Automating Outlier Detection via Meta-Learning
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)?
Connecting Graph Convolutional Networks and Graph-Regularized PCA
Based on this connection, GCN architecture, shaped by stacking graph convolution layers, shares a close relationship with stacking GPCA.
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
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.
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
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.
Coverage-based Outlier Explanation
Existing algorithms are primarily focused on detection, that is the identification of outliers in a given dataset.
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification
As such, PG-learn is a carefully-designed hybrid of random and adaptive search.
PairNorm: Tackling Oversmoothing in GNNs
The performance of graph neural nets (GNNs) is known to gradually decrease with increasing number of layers.
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and unsupervised learning problems.
Continual Rare-Class Recognition with Emerging Novel Subclasses
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.
Incorporating Privileged Information to Unsupervised Anomaly Detection
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.
Explaining Anomalies in Groups with Characterizing Subspace Rules
We consider a complementary problem that has a much sparser literature: anomaly description.
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
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.
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
Robust Semi-Supervised Classification for Multi-Relational Graphs
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.
Less is More: Building Selective Anomaly Ensembles
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.
Graph-based Anomaly Detection and Description: A Survey
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
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